Is air pollution exposure linked to household income? Spatial analysis of Community Multiscale Air Quality Model results for Madrid.

This study explores the potential correlation between income and exposure to air pollution for the city of Madrid, Spain and its neighboring municipalities. Madrid is a well-known European air pollution hotspot with a high mortality burden attributable to nitrogen dioxide (NO2) and fine particulate matter (PM2.5). Statistical analyses were carried out using electoral district level data on gross household income (GHI), and NO2 and PM2.5 concentrations in air obtained from a mesoscale air quality model for the study area. We applied linear regression, bivariate spatial correlation analysis, spatial autoregression and geographically weighted regression to explore the relationship between contaminants and income. Three different strategies were adopted to harmonize data for analysis. While some strategies suggested a link between income and air pollution, others did not, highlighting the need for multiple different approaches where uncertainty is high. Our findings offer important lessons for future spatial geographical studies of air pollution in cities worldwide. In particular we highlight the limitations of census-scale socio-economic data and the lack of non-model derived high-resolution air quality measurement data for many cities and offers lessons for policy makers on improving the integration of these types of essential public information.

Real-Time Measurements of Indoor-Outdoor Exchange of Gaseous and Particulate Atmospheric Pollutants in an Urban Area.

Air pollution is one of the greatest environmental risks to health, causing millions of deaths and deleterious health effects worldwide, especially in urban areas where citizens are exposed to high ambient levels of pollutants, also influencing indoor air quality (IAQ). Many sources of indoor air are fairly obvious and well known, but the contribution of outside sources to indoor air still leads to significant uncertainties, in particular the influence that environmental variables have on outdoor/indoor pollutant exchange mechanisms. This is a critical aspect to consider in IAQ studies. In this respect, an experimental study was performed at a public site such as a university classroom during a non-academic period in Madrid city. This includes two field campaigns, in summer (2021) and winter (2020), where instruments for measuring gases and particle air pollutants simultaneously measured outdoor and indoor real-time concentrations. This study aimed to investigate the dynamic variations in the indoor/outdoor (I/O) ratios in terms of ambient outdoor conditions (meteorology, turbulence and air quality) and indoor features (human presence or natural ventilation). The results show that the I/O ratio is pollutant-dependent. In this sense, the infiltration capacity is higher for gaseous compounds, and in the case of particles, it depends on the particle size, with a higher infiltration capacity for smaller particles (

Oxidation capacity changes in the atmosphere of large urban areas in Europe: Modelling and experimental campaigns in atmospheric simulation chambers.

Air pollution is a major concern for human health and the environment. Consequently, environmental standards have become stricter to improve air quality. Thanks to this, the ambient levels of O3 precursors such as VOCs and NOX have decreased. However, O3 levels in Europe, especially during winter, have increased, potentially impacting on atmospheric oxidation capacity and the associated chemistry of tropospheric oxidants. In this work, we focus on recent changes in the oxidation capacity of urban atmospheres. The study is conducted with the results of the CMAQ modelling system with a regional resolution with 12 × 12 km2 across the entire European continent for the winter (January) and summer (July) of 2007 and 2015. The 2015 meteorological data is used for both years to emphasise emission changes during the studied period. We scrutinise the changes in ambient concentration levels of the main tropospheric oxidants (O3 and HOX radicals) in five representative cities, Valencia, Madrid, Milan, Berlin, and The Hague. The enhanced O3 formation in winter seems to be due to the low VOC/NOX ratio, while the opposite trend in summer may be related to a relatively high ratio. Additionally, photooxidation experiments are carried out in the EUPHORE chambers to study the effect of changes in NOX concentration and NO/NO2 ratio on the variation of the given oxidants at constant VOCs concentrations. For the baseline experiments, two scenarios are selected based on the model results of 2015: two representative winter and summer days of low and high pollution in Berlin and Madrid, respectively. The role of VOC/NOX and NO/NO2 ratios on atmospheric reactivity is discussed. As a result, it is first suggested that further decreases in ambient NOX levels are required to reduce ambient O3 levels. Moreover, additional factors should be considered when designing local-specific emission abatement strategies.

The impact of wildfires on air pollution and health across land use categories in Brazil over a 16-year period.

Forest fires cause many environmental impacts, including air pollution. Brazil is a very fire-prone region where few studies have investigated the impact of wildfires on air quality and health. We proposed to test two hypotheses in this study: i) the wildfires in Brazil have increased the levels of air pollution and posed a health hazard in 2003-2018, and ii) the magnitude of this phenomenon depends on the type of land use and land cover (e.g., forest area, agricultural area, etc.). Satellite and ensemble models derived data were used as input in our analyses. Wildfire events were retrieved from Fire Information for Resource Management System (FIRMS), provided by NASA; air pollution data from the Copernicus Atmosphere Monitoring Service (CAMS); meteorological variables from the ERA-Interim model; and land use/cover data were derived from pixel-based classification of Landsat satellite images by MapBiomas. We used a framework that infers the "wildfire penalty" by accounting for differences in linear pollutant annual trends (ß) between two models to test these hypotheses. The first model was adjusted for Wildfire-related Land Use activities (WLU), considered as an adjusted model. In the second model, defined as an unadjusted model, we removed the wildfire variable (WLU). Both models were controlled by meteorological variables. We used a generalized additive approach to fit these two models. To estimate mortality associated with wildfire penalties, we applied health impact function. Our findings suggest that wildfire events between 2003 and 2018 have increased the levels of air pollution and posed a significant health hazard in Brazil, supporting our first hypothesis. For example, in the Pampa biome, we estimated an annual wildfire penalty of 0.005 µg/m3 (95%CI: 0.001; 0.009) on PM2.5. Our results also confirm the second hypothesis. We observed that the greatest impact of wildfires on PM2.5 concentrations occurred in soybean areas in the Amazon biome. During the 16 years of the study period, wildfires originating from soybean areas in the Amazon biome were associated with a total penalty of 0.64 µg/m3 (95%CI: 0.32; 0.96) on PM2.5, causing an estimated 3872 (95%CI: 2560; 5168) excess deaths. Sugarcane crops were also a driver of deforestation-related wildfires in Brazil, mainly in Cerrado and Atlantic Forest biomes. Our findings suggest that between 2003 and 2018, fires originating from sugarcane crops were associated with a total penalty of 0.134 µg/m3 (95%CI: 0.037; 0.232) on PM2.5 in Atlantic Forest biome, resulting in an estimated 7600 (95%CI: 4400; 10,800) excess deaths during the study period, and 0.096 µg/m3 (95%CI: 0.048; 0.144) on PM2.5 in Cerrado biome, resulting in an estimated 1632 (95%CI: 1152; 2112) excess deaths during the study period. Considering that the wildfire penalties observed during our study period may continue to be a challenge in the future, this study should be of interest to policymakers to prepare future strategies related to forest protection, land use management, agricultural activities, environmental health, climate change, and sources of air pollution.

A high resolution vehicular emissions inventory for Ecuador using the IVE modelling system.

Air pollutants caused by traffic has become a topic of global interest due to its impact on human health and the environment, making high-resolution emission inventories effective mechanisms for air quality management. This study proposes the development of a high-resolution inventory of vehicle emissions in Ecuador using the IVE modelling system, which was developed for its use in third world countries. The required data was collected in several provinces of the country, determining vehicle intensity, driving patterns, departure patterns, environmental variables, and vehicle technologies. To have a greater data representation, vehicles were classified into five categories according to their size, in addition three types of roads were also considered (Highways, Roads and Residential). The database was used to determine the specific power of the engine and "bines", variables that together with the emission factors are part of the calculation of IVE model. Atmospheric pollutants such as CO, VOC's and VOC Evap, NOx, SOx, PM, CO2 and CH4 were also considered, it has been identified that in Ecuador 3.66 million tons of CO were produced in 2015, with trucks representing road transportation being the largest pollutants with approximately 57.2% of the whole total. Through the spatial disaggregation it was possible to identify that the most critical areas, in terms of generation of atmospheric pollutants, are in the most densely populated cities of the country such as Quito and Guayaquil, as well as in areas near seaports and state roads, from 6:00 h, 12:00 h and 18:00 h the hours of the day in which the largest number of emissions are produced. At the end of the study, it was discovered that trucks were the ones that generated the highest emissions of atmospheric pollutants in Ecuador.

On-road vehicle emission inventory and its spatial and temporal distribution in the city of Guayaquil, Ecuador.

Emissions from mobile sources have become a major concern for health, environmental sustainability and climate change and high-resolution inventories are needed to support the design and assessment of abatement measures in urban areas. This study addresses the development of a traffic emissions inventory for Guayaquil, the second largest city in Ecuador, using the International Vehicle Emissions Model (IVE). Emissions are allocated with a spatial resolution of 1 km × 1 km and a temporal resolution of 1 h using a top-down methodology. This application combines traffic statistics already available in the city with the data from a field campaign to characterize vehicle fleet composition and activity patterns. The estimated annual emissions for the city were 237.1 kt of CO, 46.4 kt of NOx, 28.5 kt of VOC, 7.7 kt of PM10, 0.70 kt of SO2 and 4549.7 kt of CO2. 92.3 % of CO and 85.4 % of VOC were emitted by light gasoline vehicles, including private passenger vehicles and taxis, which represents 68.6 % and 8.8 %, respectively of the total fleet and contributes 52 % and 22 % of the total vehicle kilometer traveled (VKT), respectively. 48.9 % of NOx and 82 % of PM10 were emitted by the bus fleet although buses only represent 7.5 % of the total fleet and contribute 10.6 % of total VKT in the city. 41.1 % and 36.5 % of CO2 were emitted by buses and private vehicles, respectively. Even though, the average age of the fleet is below 10 years, the fleet in Guayaquil presents outdated emission standards and high emission factors. We found the higher emission rates in dense populated areas are associated to secondary roads. There is not much variability of emissions between months, but the typical daily pattern of emissions shows a peak in the morning and another in the afternoon.

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.

Assessment and statistical modelling of airborne microorganisms in Madrid.

The limited evidence available suggests that the interaction between chemical pollutants and biological particles may intensify respiratory diseases caused by air pollution in urban areas. Unlike air pollutants, which are routinely measured, records of biotic component are scarce. While pollen concentrations are daily surveyed in most cities, data related to airborne bacteria or fungi are not usually available. This work presents the first effort to understand atmospheric pollution integrating both biotic and abiotic agents, trying to identify relationships among the Proteobacteria, Actinobacteria and Ascomycota phyla with palynological, meteorological and air quality variables using all biological historical records available in the Madrid Greater Region. The tools employed involve statistical hypothesis contrast tests such as Kruskal-Wallis and machine learning algorithms. A cluster analysis was performed to analyse which abiotic variables were able to separate the biotic variables into groups. Significant relationships were found for temperature and relative humidity. In addition, the relative abundance of the biological phyla studied was affected by PM10 and O3 ambient concentration. Preliminary Generalized Additive Models (GAMs) to predict the biotic relative abundances based on these atmospheric variables were developed. The results (r = 0.70) were acceptable taking into account the scarcity of the available data. These models can be used as an indication of the biotic composition when no measurements are available. They are also a good starting point to continue working in the development of more accurate models and to investigate causal relationships.

Predicting the Olea pollen concentration with a machine learning algorithm ensemble.

Air pollution in large cities produces numerous diseases and even millions of deaths annually according to the World Health Organization. Pollen exposure is related to allergic diseases, which makes its prediction a valuable tool to assess the risk level to aeroallergens. However, airborne pollen concentrations are difficult to predict due to the inherent complexity of the relationships among both biotic and environmental variables. In this work, a stochastic approach based on supervised machine learning algorithms was performed to forecast the daily Olea pollen concentrations in the Community of Madrid, central Spain, from 1993 to 2018. Firstly, individual Light Gradient Boosting Machine (LightGBM) and artificial neural network (ANN) models were applied to predict the day of the year (DOY) when the peak of the pollen season occurs, resulting the estimated average peak date 149.1 ± 9.3 and 150.1 ± 10.8 DOY for LightGBM and ANN, respectively, close to the observed value (148.8 ± 9.8). Secondly, the daily pollen concentrations during the entire pollen season have been calculated using an ensemble of two-step GAM followed by LightGBM and ANN. The results of the prediction of daily pollen concentrations showed a coefficient of determination (r2) above 0.75 (goodness of the model following cross-validation). The predictors included in the ensemble models were meteorological variables, phenological metrics, specific site-characteristics, and preceding pollen concentrations. The models are state-of-the-art in machine learning and their potential has been shown to be used and deployed to understand and to predict the pollen risk levels during the main olive pollen season.

Health impact assessment by the implementation of Madrid City air-quality plan in 2020.

OBJECTIVES: Air pollutant concentrations in many urban areas are still above the legal and recommended limits that are set to protect the citizens' health. Madrid is one of the cities where traffic causes high NO2 levels. In this context, Madrid City Council launched the Air Quality and Climate Change Plan for the city of Madrid (Plan A), a local strategy approved by the previous government in 2017. The aim of this study was to conduct a quantitative health impact assessment to evaluate the number of premature deaths that could potentially be prevented by the implementation of Plan A in Madrid in 2020, at both citywide and within-city level. The main purpose was to support decision-making processes in order to maximize the positive health impacts from the implementation of Plan A measures. METHODS: The Regional Statistical Office provided information on population and daily mortality in Madrid. For exposure assessment, we estimated PM2.5, NO2 and O3 concentration levels for Madrid city in 2012 (baseline air-quality scenario) and 2020 (projected air-quality scenario based on the implementation of Plan A), by means of an Eulerian chemical-transport model with a spatial resolution of 1 km × 1 km and 30 vertical levels. We used the concentration-response functions proposed by two relevant WHO projects to calculate the number of attributable annual deaths corresponding to all non-accidental causes (ICD-10: A00-R99) among all-ages and the adult population (>30 years old) for each district and for Madrid city overall. This health impact assessment was conducted dependant on health-data availability. RESULTS: In 2020, the implementation of Plan A would imply a reduction in the Madrid citywide annual mean PM2.5 concentration of 0.6 µg/m3 and 4.0 µg/m3 for NO2. In contrast, an increase of 1 µg/m3 for O3 would be expected. The annual number of all-cause deaths from long-term exposure (95% CI) that could be postponed in the adult population by the expected air-pollutant concentration reduction was 88 (57-117) for PM2.5 and 519 (295-750) for NO2; short-term exposure accounted for 20 (7-32) for PM2.5 and 79 (47-111) for NO2 in the total population. According to the spatial distribution of air pollutants, the highest mortality change estimations were for the city centre - including Madrid Central and mainly within the M-30 ring road -, as compared to peripheral districts. The positive health impacts from the reductions in PM2.5 and NO2 far exceeded the adverse mortality effects expected from the increase in O3. CONCLUSIONS: Effective implementation of Plan A measures in Madrid city would bring about an appreciable decline in traffic-related air-pollutant concentrations and, in turn, would lead to significant health-related benefits.

Impact of weather changes on air quality and related mortality in Spain over a 25 year period [1993-2017].

Climate change is a major public health concern. In addition to its direct impacts on temperature patterns and extreme weather events, climate change affects public health indirectly through its influence on air quality. Pollution trends are not only affected by emissions changes but also by weather changes. In this paper we analyze air quality trends in Spain of important air pollutants (C6H6, CO, NO2, NOx, O3, PM10, PM2.5, and SO2) recorded during the last 25 years, from 1993 to 2017. We found substantial reductions in ambient concentration levels for all the pollutants studied except for O3. To assess the influence of recent weather changes on air quality trends we applied generalized additive models (GAMs) using nonparametric smoothing; with and without adjusting for weather parameters including temperature, wind speed, humidity and precipitation frequency. The difference of annual slopes estimated by the models without and with adjusting for these meteorological variables represents the impact of weather changes on pollutant trends, i.e. the 'weather penalty'. The analyses were seasonally and geographically stratified to account for temporal and regional differences across Spain. The results were meta-analyzed to estimate weather penalties on ambient concentration trends at a national level as well as the impact on mortality for the most relevant pollutants. We found significant penalties for most pollutants, implying that air quality would have improved even more during our study period if weather conditions had remained constant. The largest weather influences were found for PM10, with seasonal penalties up to 22 µg⋅m-3 accumulated over the 25-year period in some regions. The national meta-analysis shows penalties of 0.060 µg⋅m-3 per year (95% Confidence Interval, CI: 0.004, 0.116) in cold months and 0.127 µg⋅m-3 per year (95% CI: 0.089, 0.164) in warm months. Penalties of this magnitude would correspond to 129 annual deaths (95% CI: 25, 233), i.e. approximately 3200 deaths over the 25-year period in Spain. According to our results, the health benefits of recent emission abatements for this pollutant in Spain would have been up to 10% greater if weather conditions had remained constant during the last 25 years.

Population dynamics based on mobile phone data to improve air pollution exposure assessments.

Air pollution is one of the greatest challenges cities are facing today and improving air quality is a pressing need to reduce negative health impacts. In order to efficiently evaluate which are the most appropriate policies to reduce the impact of urban pollution sources (such as road traffic), it is essential to conduct rigorous population exposure assessments. One of the main limitations associated with those studies is the lack of information about population distribution in the city along the day (population dynamics). The pervasive use of mobile devices in our daily lives opens new opportunities to gather large amounts of anonymized and passively collected geolocation data allowing the analysis of population activity and mobility patterns. This study presents a novel methodology to estimate population dynamics from mobile phone data based on a user-centric mobility model approach. The methodology was tested in the city of Madrid (Spain) to evaluate population exposure to NO2. A comparison with traditional census-based methods shows relevant discrepancies at disaggregated levels and highlights the need to incorporate mobility patterns into population exposure assessments.

Impact of halogen chemistry on summertime air quality in coastal and continental Europe: application of the CMAQ model and implications for regulation.

Halogen (Cl, Br, and I) chemistry has been reported to influence the formation of secondary air pollutants. Previous studies mostly focused on the impact of chlorine species on air quality over large spatial scales. Very little attention has been paid to the effect of the combined halogen chemistry on air quality over Europe and its implications for control policy. In the present study, we apply a widely used regional model, the Community Multiscale Air Quality Modeling System (CMAQ), incorporated with the latest halogen sources and chemistry, to simulate the abundance of halogen species over Europe and to examine the role of halogens in the formation of secondary air pollution. The results suggest that the CMAQ model is able to reproduce the level of O3, NO2, and halogen species over Europe. Chlorine chemistry slightly increases the levels of OH, HO2, NO3, O3, and NO2 and substantially enhances the level of the Cl radical. Combined halogen chemistry induces complex effects on OH (ranging from -0.023 to 0.030 pptv) and HO2 (in the range of -3.7 to 0.73 pptv), significantly reduces the concentrations of NO3 (as much as 20 pptv) and O3 (as much as 10 ppbv), and decreases NO2 in highly polluted regions (as much as 1.7 ppbv); it increases NO2 (up to 0.20 ppbv) in other areas. The maximum effects of halogen chemistry occur over oceanic and coastal regions, but some noticeable impacts also occur over continental Europe. Halogen chemistry affects the number of days exceeding the European Union target threshold for the protection of human beings and vegetation from ambient O3. In light of the significant impact of halogen chemistry on air quality, we recommend that halogen chemistry be considered for inclusion in air quality policy assessments, particularly in coastal cities.

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.

Application of a short term air quality action plan in Madrid (Spain) under a high-pollution episode - Part I: Diagnostic and analysis from observations.

Exceedances of NO2 hourly limit value (200 µg·m-3) imply the need to implement short term action plans to avoid adverse effects on human health in urban areas. The Madrid City Council applied the stage 3 of the NO2 protocol during a high-pollution episode under stable meteorological conditions on December 2016 for the first time. This included road traffic access restrictions to the city centre (50% of conventional private vehicles based on plate numbers). In this contribution we analyse different meteorological and air quality observations, including non-standard parameters (such as number of ultrafine particles and remote sensing techniques MAXDOAS) for a better understanding of the effectivity of short-term emission abatement measures under real conditions and to identify options to improve the NO2 protocol in the future. According to our results, the inversion base height computed from vertical temperature soundings is a meaningful index to anticipate very unfavourable conditions and trigger the actions included in the protocol. The analysis of the concentration levels of the main pollutants from the Madrid air quality monitoring network indicate that only stage 3 of the protocol had a significant effect on NO2 maximum concentrations. The restrictions applied may have prevented NO2 concentrations to further increase in the city centre (up to 15%) although pollution levels in the city outskirts, outside the area directly affected by the traffic restrictions, remained unchanged or may have been slightly increased. Nonetheless, further studies are needed to estimate more precisely the effect of the measures taken and to assess potential trade-offs. Our results suggest that emissions play an important role also under very strong stability conditions although drastic measures are needed to achieve a significant impact. This highlights the importance of an appropriate timing for short-term actions and the need of permanent abatement measures related to air quality plans and policies.

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.

The influence of ocean halogen and sulfur emissions in the air quality of a coastal megacity: The case of Los Angeles.

The oceans are the main source of natural halogen and sulfur compounds, which have a significant influence on the oxidizing capacity of the marine atmosphere; however, their impact on the air quality of coastal cities is currently unknown. We explore the effect of marine halogens (Cl, Br and I) and dimethyl sulfide (DMS) on the air quality of a large coastal city through a set of high-resolution (4-km) air quality simulations for the urban area of Los Angeles, US, using the Community Multiscale Air Quality (CMAQ model). The results indicate that marine halogen emissions decrease ozone and nitrogen dioxide levels up to 5ppbv and 2.5ppbv, respectively, in the city of Los Angeles. Previous studies suggested that the inclusion of chlorine in air quality models leads to the generation of ozone in urban areas through photolysis of nitryl chloride (ClNO2). However, we find that when considering the chemistry of Cl, Br and I together the net effect is a reduction of surface ozone concentrations. Furthermore, combined ocean emissions of halogens and DMS cause substantial changes in the levels of key urban atmospheric oxidants such as OH, HO2 and NO3, and in the composition and mass of fine particles. Although the levels of ozone, NO3 and HOx are reduced, we find a 10% increase in secondary organic aerosol (SOA) mean concentration, attributed to the increase in aerosol acidity and sulfate aerosol formation when combining DMS and bromine. Therefore, this new pathway for enhanced SOA formation may potentially help with current model under predictions of urban SOA. Although further observations and research are needed to establish these preliminary conclusions, this first city-scale investigation suggests that the inclusion of oceanic halogens and DMS in air quality models may improve regional air quality predictions over coastal cities around the world.

Microscale traffic simulation and emission estimation in a heavily trafficked roundabout in Madrid (Spain).

This paper presents the evaluation of emissions from vehicle operations in a domain of 300m×300m covering a complex urban roundabout with high traffic density in Madrid. Micro-level simulation was successfully applied to estimate the emissions on a scale of meters. Two programs were used: i) VISSIM to simulate the traffic on the square and to compute velocity-time profiles; and ii) VERSIT+micro through ENVIVER that uses VISSIM outputs to compute the related emissions at vehicle level. Data collection was achieved by a measurement campaign obtaining empirical data of vehicle flows and traffic intensities. Twelve simulations of different traffic situations (scenarios) were conducted, representing different hours from several days in a week and the corresponding NOX and PM10 emissions were estimated. The results show a general reduction on average speeds for higher intensities due to braking-acceleration patterns that contribute to increase the average emission factor and, therefore, the total emissions in the domain, especially on weekdays. The emissions are clearly related to traffic volume, although maximum emission scenario does not correspond to the highest traffic intensity due to congestion and variations in fleet composition throughout the day. These results evidence the potential that local measures aimed at alleviating congestion may have in urban areas to reduce emissions. In general, scenario-averaged emission factors estimated with the VISSIM-VERSIT+micro modelling system fitted well those from the average-speed model COPERT, used as a preliminary validation of the results. The largest deviations between these two models occur in those scenarios with more congestion. The design and resolution of the microscale modelling system allow to reflect the impact of actual traffic conditions on driving patterns and related emissions, making it useful for the design of mitigation measures for specific traffic hot-spots.