Towards sustainable and net-zero cities: A review of environmental modelling and monitoring tools for optimizing emissions reduction strategies for improved air quality in urban areas.

Climate change is a defining challenge for today's society and its consequences pose a great threat to humanity. Cities are major contributors to climate change, accounting for over 70% of global greenhouse gas emissions. With urbanization occurring at a rapid rate worldwide, cities will play a key role in mitigating emissions and addressing climate change. Greenhouse gas emissions are strongly interlinked with air quality as they share emission sources. Consequently, there is a great opportunity to develop policies which maximize the co-benefits of emissions reductions on air quality and health. As such, a narrative meta-review is conducted to highlight state-of-the-art monitoring and modelling tools which can inform and monitor progress towards greenhouse gas emission and air pollution reduction targets. Urban greenspace will play an important role in the transition to net-zero as it promotes sustainable and active transport modes. Therefore, we explore advancements in urban greenspace quantification methods which can aid strategic developments. There is great potential to harness technological advancements to better understand the impact of greenhouse gas reduction strategies on air quality and subsequently inform the optimal design of these strategies going forward. An integrated approach to greenhouse gas emission and air pollution reduction will create sustainable, net-zero and healthy future cities.

Applications of remote sensing vis-à-vis machine learning in air quality monitoring and modelling: a review.

Environmental contamination especially air pollution is an exponentially growing menace requiring immediate attention, as it lingers on with the associated risks of health, economic and ecological crisis. The special focus of this study is on the advances in Air Quality (AQ) monitoring using modern sensors, integrated monitoring systems, remote sensing and the usage of Machine Learning (ML), Deep Learning (DL) algorithms, artificial neural networks, recent computational techniques, hybridizing techniques and different platforms available for AQ modelling. The modern world is data-driven, where critical decisions are taken based on the available and accessible data. Today's data analytics is a consequence of the information explosion we have reached. The current research also tends to re-evaluate its scope with data analytics. The emergence of artificial intelligence and machine learning in the research scenario has radically changed the methodologies and approaches of modern research. The aim of this review is to assess the impact of data analytics such as ML/DL frameworks, data integration techniques, advanced statistical modelling, cloud computing platforms and constantly improving optimization algorithms on AQ research. The usage of remote sensing in AQ monitoring along with providing enormous datasets is constantly filling the spatial gaps of ground stations, as the long-term air pollutant dynamics is best captured by the panoramic view of satellites. Remote sensing coupled with the techniques of ML/DL has the most impact in shaping the modern trends in AQ research. Current standing of research in this field, emerging trends and future scope are also discussed.

Vertical aerosol data assimilation technology and application based on satellite and ground lidar: A review and outlook.

Observations and numerical models are mainly used to investigate the spatiotemporal distribution and vertical structure characteristics of aerosols to understand aerosol pollution and its effects. However, the limitations of observations and the uncertainties of numerical models bias aerosol calculations and predictions. Data assimilation combines observations and numerical models to improve the accuracy of the initial, analytical fields of models and promote the development of atmospheric aerosol pollution research. Numerous studies have been conducted to integrate multi-source data, such as aerosol optical depth and aerosol extinction coefficient profile, into various chemical transport models using various data assimilation algorithms and have achieved good assimilation results. The definition of data assimilation and the main algorithms will be briefly presented, and the progress of aerosol assimilation according to two types of aerosol data, namely, aerosol optical depth and extinction coefficient, will be presented. The application of vertical aerosol data assimilation, as well as the future trends and challenges of aerosol data assimilation, will be further analysed.

Source apportionment of air pollution in European urban areas: Lessons from the ClairCity project.

Air pollution has become a major threat to human health in the last decades, with an increase of acute air pollution episodes in many cities worldwide. Source apportionment modelling provides valuable information on the contribution from different emission source sectors and source regions to distinct air pollutants concentrations. In this study, the CAMx model, with its PSAT tool, was applied to quantify the contribution of multiple source areas, categories and pollutant types to ambient air pollution, namely to PM and NO2 concentrations, over six European urban areas: Bristol (United Kingdom), Amsterdam (The Netherlands), Ljubljana (Slovenia), Liguria Region (Italy), Sosnowiec (Poland) and Aveiro Region (Portugal). Results indicate overall higher annual NO2 and PM concentrations located in the urban centres of the case studies. A comparison between the different areas showed that Liguria is the region with highest NO2 annual mean concentrations, while Ljubljana, Liguria Region and Sosnowiec are the case studies with the highest PM annual mean concentrations. The annual average contributions denote a major influence from road transport to NO2 concentrations, with up to 50%, except in Aveiro region, where road transport presents a lower contribution to NO2 concentrations, and the greatest contributor is the industrial combustion and processes sector with 45%. These results indicate a negligible contribution of the transboundary transport to NO2 concentrations, highlighting the relevance of local sources, while for PM concentrations the transboundary transport is the major contributor. The results highlight the relevance of long-range transport of PM across Europe. The transboundary transport reduces its importance during winter, when residential and commercial combustion increases its contribution. In the case of the Aveiro region, the industrial combustion and processes sector also plays an important contribution to PM concentrations.

Evaluating the impact of "Sustainable Urban Mobility Plans" on urban background air quality.

Air quality in European cities is still a challenge, with various urban areas frequently exceeding the PM2.5 and NO2 concentration levels allowed by the European Union Air Quality Standards. This is a problem both in terms of legislation compliance, but also in terms of health of citizens, as it has been recently estimated that 400 to 450 thousand people die prematurely every year due to poor air quality. Air quality in cities can be improved with a number of interventions, at different sectoral (industry, traffic, residential, etc …) and geographical (international, European, national, local, etc.) levels. In this paper we explore the potential of city level plans to improve mobility and air quality (excluding electro-mobility options, not considered in this study). We applied the "Sustainable Urban Mobility Plans" (SUMPs) framework to 642 cities in Europe and modelled how the measures they include may impact at first on mobility and emissions at urban level, and then on urban background concentrations of PM2.5 and NO2. Results show that annual averages moderately improve for both pollutants, with reductions of urban background concentrations up to 2% for PM2.5 and close to 4% for NO2. The impact on NO2 at street level (that will be higher than on urban background) is not evaluated in this work. The air quality improvement of the simulated SUMP would only partially alleviate air quality problems in urban areas, but such a reduction in the emissions of air pollutants should still be considered as a positive result of SUMPs, given that they correspond to a set of low-cost measures that can be implemented at local level. Furthermore, the introduction of electro-mobility options (not considered here) would increase the impact on air quality. Other types of benefits, such as reduced fuel consumption, greenhouse gas emissions, higher impact at street level or accident rates reduction further add to the overall positive impact.

The impact of US wildland fires on ozone and particulate matter: a comparison of measurements and CMAQ model predictions from 2008 to 2012.

Wildland fire emissions are routinely estimated in the US Environmental Protection Agency's National Emissions Inventory, specifically for fine particulate matter (PM2.5) and precursors to ozone (O3); however, there is a large amount of uncertainty in this sector. We employ a brute-force zero-out sensitivity method to estimate the impact of wildland fire emissions on air quality across the contiguous US using the Community Multiscale Air Quality (CMAQ) modelling system. These simulations are designed to assess the importance of wildland fire emissions on CMAQ model performance and are not intended for regulatory assessments. CMAQ ver. 5.0.1 estimated that fires contributed 11% to the mean PM2.5 and less than 1% to the mean O3 concentrations during 2008-2012. Adding fires to CMAQ increases the number of 'grid-cell days' with PM2.5 above 35 µg m-3 by a factor of 4 and the number of grid-cell days with maximum daily 8-h average O3 above 70 ppb by 14%. Although CMAQ simulations of specific fires have improved with the latest model version (e.g. for the 2008 California wildfire episode, the correlation r = 0.82 with CMAQ ver. 5.0.1 v. r = 0.68 for CMAQ ver. 4.7.1), the model still exhibits a low bias at higher observed concentrations and a high bias at lower observed concentrations. Given the large impact of wildland fire emissions on simulated concentrations of elevated PM2.5 and O3, improvements are recommended on how these emissions are characterised and distributed vertically in the model.

Integrated modelling approach for the evaluation of low emission zones.

Low emission zones (LEZ) are areas where the most polluting vehicles are restricted or deterred from entering. In recent years, LEZ became a popular option to reduce traffic-related air pollution and have been implemented in many cities worldwide, notably in Europe. However, the evidence about their effectiveness is inconsistent. This calls for the development of tools to evaluate ex-ante the air quality impacts of a LEZ. The integrated modelling approach we propose in this paper aims to respond to this call. It links a transportation model with an emissions model and an air quality model operating over a GIS-based platform. Through the application of the approach, it is possible to estimate the changes induced by the creation of a LEZ applied to private cars with respect to air pollution levels not only inside the LEZ, but also, more generally, in the city where it is located. The usefulness of the proposed approach was demonstrated for a case study involving the city of Coimbra (Portugal), where the creation of a LEZ is being sought to mitigate the air quality problems that its historic centre currently faces. The main result of this study was that PM10 and NO2 emissions from private cars would decrease significantly inside the LEZ (63% and 52%, respectively) but the improvement in air quality would be small and exceedances to the air pollution limits adopted in the European Union would not be fully avoided. In contrast, at city level, total emissions increase and a deterioration of air quality is expected to occur.

Atmospheric particulate matter retention capacity of bark and leaves of urban tree species.

Urban vegetation can effectively filter and adsorb particulate matter (PM). However, limited studies have been conducted on the PM retention capacity of tree barks. This study investigated the ability of five common urban tree species in the Yangtze River Delta region to retain PM through their barks and leaves by conducting a 14-day tree PM retention experiment on the five tree species during autumn and winter. The results showed that (1) the PM retention per unit area of bark was 6.9 times and 11.8 times higher than that of leaves during autumn and winter, respectively; (2) when considering total surface area, bark and leaves exhibited comparable PM retention capacities at the whole-plant scale; (3) the ability of bark to retain PM is species-specific, which can be attributed to different bark morphology among different tree species; and (4) bark and leaves exhibited distinct preferences for retaining PM of different particle sizes, even when exposed to similar environmental conditions. This study highlights the remarkable ability of tree bark to PM removal and provides valuable insights into the role of urban trees in mitigating PM pollution. Furthermore, these findings can provide valuable insights into studies on dry deposition modelling, urban planning, and green space management strategies.

A bottom-up agricultural emissions inventory and its analysis via CMAQ and IASI-NH3.

The global increase in population has led to higher emissions from livestock and synthetic fertilizers. This study investigates the impact of agricultural ammonia emissions on NH3 concentrations and provides insights into PM2.5 levels and their components in agriculturally intensified areas. We developed a bottom-up emission inventory focused on fertilizer application over croplands and livestock, instead of relying on the EMEP database. This approach utilized an improved spatial and temporal distribution of these emissions. We compared annual total NH3 emissions from livestock and fertilizer, estimated at 598.5 kt and 187.2 kt in the EMEP inventory (Base case), and 245.2 kt and 536 kt in the bottom-up inventory (Scenario case). Using the CMAQ modelling framework, we estimated atmospheric concentrations for both cases and evaluated the model results by comparing them with IASI-NH3 satellite retrievals. This comparison revealed significant differences in column concentrations between the Base and Scenario cases, with the Scenario case showing substantial improvement. Over a period of seven months, which contributed 80 % of the annual agricultural emissions for the Scenario case, the domain averages of NH3 were 3.02 × 1015, 4.15 × 1015, and 4.17 × 1015 molecules/cm2 for the Base and Scenario cases and IASI-NH3, respectively. The Scenario case closely matched IASI measurements, indicating a more accurate representation of NH3 emissions and concentrations. This enhanced reliability underscores the effectiveness of the bottom-up inventory approach. Additionally, using the CMAQ model, we found that in the IASI hotspots, the averages were 1.67 µg/m3 for sulfate, 0.57 µg/m3 for nitrate, and 0.62 µg/m3 for ammonium, with a total PM2.5 mean of 10.45 µg/m3.

Analysis of emissions-driven changes in the oxidation capacity of the atmosphere in Europe.

Anthropogenic emissions in Europe have been gradually reduced thanks to a combination of factors, including restrictive regulation and policy implementation, fuel switching, technological developments, and improved energy efficiencies. Many measures have been specifically introduced to meet the annual and hourly limit value of NO2 for the protection of human health, mainly targeting traffic emissions. Due to NOX reduction policies in Europe, NO2 levels have generally declined, but O3 concentrations have been found to increase. This phenomenon would cause changes in the oxidant capacity of the atmosphere, altering the concentration of tropospheric oxidants in urban areas. The Community Multiscale Air Quality (CMAQ) modelling system has been used to study concentration changes of NO2, O3 and the main radicals in Europe between 2007 and 2015 for two months representatives of winter and summer conditions (January and July). In addition to describing the general situation in Europe, variations in pollutants along with NOX emission changes over 67 large European cities have been analysed by means of statistical methods. NOX emissions and NO2 concentrations decreased in both seasons during the period in all the selected cities. In most of them O3 concentrations increased in winter but decreased in summer. The concentration of the OH radical, the main oxidant during the daytime, shows an increase in winter. This is also the case for the main cities in summer although we found a general decrease in continent for this season. The NO3 radical, the main night-time oxidant, was found to increase in winter and decrease in summer. HNO3 shows a concentration decline in both seasons. The studied cities are classified in five groups by means of k-mean clustering procedure. We identified five groups with specific patterns, suggesting that the oxidant capacity of the European urban atmospheres has reacted differently to NOX emission abatement policies.

Lessons learnt for air pollution mitigation policies from the COVID-19 pandemic: The Italian perspective.

Policies to improve air quality need to be based on effective plans for reducing anthropogenic emissions. In 2020, the outbreak of COVID-19 pandemic resulted in significant reductions of anthropogenic pollutant emissions, offering an unexpected opportunity to observe their consequences on ambient concentrations. Taking the national lockdown occurred in Italy between March and May 2020 as a case study, this work tries to infer if and what lessons may be learnt concerning the impact of emission reduction policies on air quality. Variations of NO2, O3, PM10 and PM2.5 concentrations were calculated from numerical model simulations obtained with business as usual and lockdown specific emissions. Both simulations were performed at national level with a horizontal resolution of 4 km, and at local level on the capital city Rome at 1 km resolution. Simulated concentrations showed a good agreement with in-situ observations, confirming the modelling systems capability to reproduce the effects of emission reductions on ambient concentration variations, which differ according to the individual air pollutant. We found a general reduction of pollutant concentrations except for ozone, that experienced an increase in Rome and in the other urban areas, and a decrease elsewhere. The obtained results suggest that acting on precursor emissions, even with sharp reductions like those experienced during the lockdown, may lead to significant, albeit complex, reduction patterns for secondary pollutant concentrations. Therefore, to be more effective, reduction measures should be carefully selected, involving more sectors than those related to mobility, such as residential and agriculture, and integrated on different scales.

Understanding temporal and spatial changes of O3 or NO2 concentrations combining multivariate data analysis methods and air quality transport models.

The application of the multivariate curve resolution method to the analysis of temporal and spatial data variability of hourly measured O3 and NO2 concentrations at nineteen air quality monitoring stations across Catalonia, Spain, during 2015 is shown. Data analyzed included ground-based experimental measurements and predicted concentrations by the CALIOPE air quality modelling system at three horizontal resolutions (Europe at 12 × 12 km2, Iberian Peninsula at 4 × 4 km2 and Catalonia at 1 × 1 km2). Results obtained in the analysis of these different data sets allowed a better understanding of O3 and NO2 concentration changes as a sum of a small number of different contributions related to daily sunlight radiation, seasonal dynamics, traffic emission patterns, and local station environments (urban, suburban and rural). The evaluation of O3 and NO2 concentrations predicted by the CALIOPE system revealed some differences among data sets at different spatial resolutions. NO2 predictions, showed in general a better performance than O3 predictions for the three model resolutions, specially at urban stations. Our results confirmed that the application of the trilinearity constraint during the multivariate curve resolution factor analysis decomposition of the analyzed data sets is a useful tool to facilitate the understanding of the resolved variability sources.

GIS-based air quality modelling: spatial prediction of PM10 for Selangor State, Malaysia using machine learning algorithms.

Rapid urbanization has caused severe deterioration of air quality globally, leading to increased hospitalization and premature deaths. Therefore, accurate prediction of air quality is crucial for mitigation planning to support urban sustainability and resilience. Although some studies have predicted air pollutants such as particulate matter (PM) using machine learning algorithms (MLAs), there is a paucity of studies on spatial hazard assessment with respect to the air quality index (AQI). Incorporating PM in AQI studies is crucial because of its easily inhalable micro-size which has adverse impacts on ecology, environment, and human health. Accurate and timely prediction of the air quality index can ensure adequate intervention to aid air quality management. Therefore, this study undertakes a spatial hazard assessment of the air quality index using particulate matter with a diameter of 10 µm or lesser (PM10) in Selangor, Malaysia, by developing four machine learning models: eXtreme Gradient Boosting (XGBoost), random forest (RF), K-nearest neighbour (KNN), and Naive Bayes (NB). Spatially processed data such as NDVI, SAVI, BU, LST, Ws, slope, elevation, and road density was used for the modelling. The model was trained with 70% of the dataset, while 30% was used for cross-validation. Results showed that XGBoost has the highest overall accuracy and precision of 0.989 and 0.995, followed by random forest (0.989, 0.993), K-nearest neighbour (0.987, 0.984), and Naive Bayes (0.917, 0.922), respectively. The spatial air quality maps were generated by integrating the geographical information system (GIS) with the four MLAs, which correlated with Malaysia's air pollution index. The maps indicate that air quality in Selangor is satisfactory and posed no threats to health. Nevertheless, the two algorithms with the best performance (XGBoost and RF) indicate that a high percentage of the air quality is moderate. The study concludes that successful air pollution management policies such as green infrastructure practice, improvement of energy efficiency, and restrictions on heavy-duty vehicles can be adopted in Selangor and other Southeast Asian cities to prevent deterioration of air quality in the future.

Evaluation of the Effectiveness of the National Clean Air Programme in Terms of Health Impacts from Exposure to PM2.5 and NO2 Concentrations in Poland.

The health impact of air quality has recently become an emerging issue. Many regions, especially densely populated regions, have deteriorating air quality. The primary source of pollution in Poland is the municipal sector. Air pollutants have a negative impact on human health, contribute to premature deaths, and are the cause of various diseases. Over recent years, Europe's air quality has largely improved due to several measures to reduce pollutant emissions. The following paper presents the impact of annual average PM2.5 and NO2 concentrations on premature deaths in Poland in 2019. Exposure to PM2.5 caused more than 19 000 premature deaths, and exposure to NO2 contributed to around 6000 premature deaths in 2019. Taking 2019 as a baseline, the impact of the envisaged implementation of the national Clean Air Programme on the number of premature deaths is analysed. Implementing the mitigation measures focused on replacing inefficient stoves and boilers in individual households would significantly improve air quality, mainly particulate matter. Reducing PM2.5 concentrations would reduce premature deaths by around 3000 cases, while for NO2, the number of premature deaths would not change much.

To what extent the traffic restriction policies applied in Barcelona city can improve its air quality?

Barcelona city (Spain) is applying a series of traffic restriction measures that aim at renewing and reducing the amount of circulating vehicles to improve air quality. The measures include changes in the built environment to reduce private vehicle space in specific areas through the so-called "superblocks" and tactical urban planning actions, along with the implementation of a city-wide Low Emission Zone (LEZ) that restricts the entry of the most polluting vehicles to the city. Our study quantifies the impact of these measures in the greater area of Barcelona combining a coupled macroscopic traffic and pollutant emission model with a multi-scale air quality model. Our modelling system allows estimating the effect of different traffic restrictions upon traffic and the associated emissions and air quality levels at a very high resolution (20 m). The measures were evaluated both individually and collectively to assess both their relative and overall impact upon emissions and air quality. We show that in the absence of traffic demand reductions, the application of isolated measures that reduce private vehicle space, either through superblocks or tactical urban planning, have no overall emission impacts; only localized street-level NOx positive and negative changes (±17%) are found due to traffic re-routing and the generation of new bottlenecks. It is only when these measures are combined with optimistic fleet renewal as a result of the LEZ implementation and demand reductions, that relevant global emission reductions in NOx are obtained (-13% and -30%, respectively) with estimated NO2 reductions of -36% and -23% at the two traffic air quality monitoring stations. Despite the potential improvements, our simulations suggest that current measures are insufficient to comply with EU air quality standards and that further traffic restriction policies to reduce traffic demand are needed.

How important are maritime emissions for the air quality: At European and national scale.

Due to its dependence on fossil fuel combustion, emissions from the marine transport sector can significantly contribute to air pollution. This work aims to evaluate the impact of maritime transport emissions on air quality in Portugal using a numerical air quality modelling approach, with high-resolution emission data. Emissions from the European TNO inventory were compiled and pre-processed at hourly and high spatial (∼3 × 3 km2) resolutions. Scenarios with and without these maritime emissions were then simulated with the WRF-CHIMERE modelling system, extensively tested and validated for Portugal domain, in order to evaluate their impact on air quality. A simulation was performed for one year (2016) and the resulting differences were analysed in terms of spatial distribution, time series and deltas. The main deltas for NO2 and PM10 are located over international shipping routes and major ports, while O3 concentrations are impacted in a larger area. The modelling results also indicate that shipping emissions are responsible for deltas in the concentration of NO2 higher than 20% over specific urban areas located in the west coast of Portugal, and less than 5% for PM10. For O3 the relative contribution is low (around 2%) but this contribution is also observed at locations more than 50 km from the coast.

Application of a short term air quality action plan in Madrid (Spain) under a high-pollution episode - Part II: Assessment from multi-scale modelling.

Air pollution continues to be one of the main issues in urban areas. In addition to air quality plans and emission abatement policies, additional measures for high pollution episodes are needed to avoid exceedances of hourly limit values under unfavourable meteorological conditions such as the Madrid's short-term action NO2 protocol. In December 2016 there was a strong atmospheric stability episode that turned out in generalized high NO2 levels, causing the stage 3 of the NO2 protocol to be triggered for the first time in Madrid (29th December). In addition to other traffic-related measures, this involves access restrictions to the city centre (50% to private cars). We simulated the episode with and without measures under a multi-scale modelling approach. A 1 km2 resolution modelling system based on WRF-SMOKE-CMAQ was applied to assess city-wide effects while the Star-CCM+ (RANS CFD model) was used to investigate the effect at street level in a microscale domain in the city centre, focusing on Gran Vía Avenue. Changes in road traffic were simulated with the mesoscale VISUM model, incorporating real flux measurements during those days. The corresponding simulations suggest that the application of the protocol during this particular episode may have prevented concentrations to increase by 24 µg·m-3 (14% respect to the hypothetical no action scenario) downtown although it may have cause NO2 to slightly increase in the city outskirts due to traffic redistribution. Speed limitation and parking restrictions alone (stages 1 and 2 respectively) have a very limited effect. The microscale simulation provides consistent results but shows an important variability at street level, with reduction above 100 µg·m-3 in some spots inside Gran Vía. Although further research is needed, these results point out the need to implement short-term action plans and to apply a consistent multi-scale modelling assessment to optimize urban air quality abatement strategies.

Estimation of PM10-bound manganese concentration near a ferromanganese alloy plant by atmospheric dispersion modelling.

Numerous studies have associated air manganese (Mn) exposure with negative health effects, primarily neurotoxic disorders. This work presents a description of the emission and dispersion of PM10-bound Mn from industrial sources in the Santander bay area, Northern Spain. A detailed day-specific emission estimation was made and assessed for the main Mn source, a manganese alloy production plant under 8 different scenarios. Dispersion analysis of PM10-bound Mn was performed using the CALPUFF model. The model was validated from an observation dataset including 101 daily samples from four sites located in the vicinities of the manganese alloy plant. Model results were in reasonable agreement with observations (r = 0.37; NMSE = 2.08; Fractional Bias = 0.44 and Modelled/Observed ratio = 1.57). Simulated and observed Mn concentrations in the study area were much higher than the guidelines proposed by the World Health Organization (WHO) and the U.S. Environmental Protection Agency (USEPA), highlighting the need to reduce the Mn concentrations in the area. Based on the analysis of the Mn source contribution from the ferromanganese alloy plant, some preventive and corrective measures are discussed at the end of the paper. This work shows that CALPUFF dispersion model can be used to predict PM10-bound Mn concentrations with reasonable accuracy in the vicinities of industrial facilities allowing the exposure assessment of the nearby population, which can be used in future epidemiological studies.

An emission processing system for air quality modelling in the Mexico City metropolitan area: Evaluation and comparison of the MOBILE6.2-Mexico and MOVES-Mexico traffic emissions.

This article describes the High-Elective Resolution Modelling Emission System for Mexico (HERMES-Mex) model, an emission processing tool developed to transform the official Mexico City Metropolitan Area (MCMA) emission inventory into hourly, gridded (up to 1km2) and speciated emissions used to drive mesoscale air quality simulations with the Community Multi-scale Air Quality (CMAQ) model. The methods and ancillary information used for the spatial and temporal disaggregation and speciation of the emissions are presented and discussed. The resulting emission system is evaluated, and a case study on CO, NO2, O3, VOC and PM2.5 concentrations is conducted to demonstrate its applicability. Moreover, resulting traffic emissions from the Mobile Source Emission Factor Model for Mexico (MOBILE6.2-Mexico) and the MOtor Vehicle Emission Simulator for Mexico (MOVES-Mexico) models are integrated in the tool to assess and compare their performance. NOx and VOC total emissions modelled are reduced by 37% and 26% in the MCMA when replacing MOBILE6.2-Mexico for MOVES-Mexico traffic emissions. In terms of air quality, the system composed by the Weather Research and Forecasting model (WRF) coupled with the HERMES-Mex and CMAQ models properly reproduces the pollutant levels and patterns measured in the MCMA. The system's performance clearly improves in urban stations with a strong influence of traffic sources when applying MOVES-Mexico emissions. Despite reducing estimations of modelled precursor emissions, O3 peak averages are increased in the MCMA core urban area (up to 30ppb) when using MOVES-Mexico mobile emissions due to its VOC-limited regime, while concentrations in the surrounding suburban/rural areas decrease or increase depending on the meteorological conditions of the day. The results obtained suggest that the HERMES-Mex model can be used to provide model-ready emissions for air quality modelling in the MCMA.

A cost-efficiency and health benefit approach to improve urban air quality.

When ambient air quality standards established in the EU Directive 2008/50/EC are exceeded, Member States are obliged to develop and implement Air Quality Plans (AQP) to improve air quality and health. Notwithstanding the achievements in emission reductions and air quality improvement, additional efforts need to be undertaken to improve air quality in a sustainable way - i.e. through a cost-efficiency approach. This work was developed in the scope of the recently concluded MAPLIA project "Moving from Air Pollution to Local Integrated Assessment", and focuses on the definition and assessment of emission abatement measures and their associated costs, air quality and health impacts and benefits by means of air quality modelling tools, health impact functions and cost-efficiency analysis. The MAPLIA system was applied to the Grande Porto urban area (Portugal), addressing PM10 and NOx as the most important pollutants in the region. Four different measures to reduce PM10 and NOx emissions were defined and characterized in terms of emissions and implementation costs, and combined into 15 emission scenarios, simulated by the TAPM air quality modelling tool. Air pollutant concentration fields were then used to estimate health benefits in terms of avoided costs (external costs), using dose-response health impact functions. Results revealed that, among the 15 scenarios analysed, the scenario including all 4 measures lead to a total net benefit of 0.3M€·y(-1). The largest net benefit is obtained for the scenario considering the conversion of 50% of open fire places into heat recovery wood stoves. Although the implementation costs of this measure are high, the benefits outweigh the costs. Research outcomes confirm that the MAPLIA system is useful for policy decision support on air quality improvement strategies, and could be applied to other urban areas where AQP need to be implemented and monitored.