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Very unhealthy air quality is consistently connected with numerous diseases. Appropriate extreme analysis and accurate predictions are in rising demand for exploring potential linked causes and for providing suggestions for the environmental agency in public policy strategy. This paper aims to model the spatial and temporal pattern of both moderate and extremely poor PM 10 concentrations (of daily mean) collected from 342 representative monitors distributed throughout mainland Spain from 2017 to 2021. We first propose and compare a series of Bayesian hierarchical generalized extreme models of annual maxima PM 10 concentrations, including both the fixed effect of altitude, temperature, precipitation, vapour pressure and population density, as well as the spatio-temporal random effect with the Stochastic Partial Differential Equation (SPDE) approach and a lag-one dynamic auto-regressive component (AR(1)). Under WAIC, DIC and other criteria, the best model is selected with good predictive ability based on the first four-year data (2017–2020) for training and the last-year data (2021) for testing. We bring the structure of the best model to establish the joint Bayesian model of annual mean and annual maxima PM 10 concentrations and provide evidence that certain predictors (precipitation, vapour pressure and population density) influence comparably while the other predictors (altitude and temperature) impact reversely in the different scaled PM 10 concentrations. The findings are applied to identify the hot-spot regions with poor air quality using excursion functions specified at the grid level. It suggests that the community of Madrid and some sites in northwestern and southern Spain are likely to be exposed to severe air pollution, simultaneously exceeding the warning risk threshold. [ABSTRACT FROM AUTHOR]

The measurement of the CO2 concentration has a wide range of applications. Traditionally, it has been used to assess air quality, with other applications linked to the experimental assessment of occupancy patterns and air renewal rates. More recently, the worldwide dissemination of COVID-19 establishing a relationship between infection risk and the mean CO2 level has abruptly led to the measurement of the CO2 concentration in order to limit the spread of this respiratory disease in the indoor environment. Therefore, the extensive application of this measurement outside of traditional air quality assessment requires an in-depth analysis of the suitability of these sensors for such modern applications. This paper discusses the performance of an array of commercial wall-mounted CO2 sensors, focusing on their application to obtain occupancy patterns and air renovation rates. This study is supported by several long-term test campaigns conducted in an in-use office building located in south-eastern Spain. The results show a spread of 19–101 ppm, with a drift of 28 ppm over 5 years, an offset of 2–301 ppm and fluctuations up to 80 ppm in instantaneous measurements not related to concentration changes. It is proposed that values averaged over 30 min, using a suitable reference value, be used to avoid erroneous results when calibration is not feasible. [ABSTRACT FROM AUTHOR]

This paper explores the relationship between air pollution, climate and reported subjective well-being (or happiness) in Spanish regions. The results show that, after controlling for most of the socio-economic variables affecting happiness, there are still significant regional differences in subjective well-being. Evidence also suggests that climate and air pollution variables play a significant role in explaining these regional differences in happiness. The analysis also allows us to calculate the monetary value of air quality and climate, deriving the average marginal rate of substitution between income and air quality and climate for the Spanish regions. [ABSTRACT FROM AUTHOR]

The design of emission abatement measures to effectively reduce high ground-level ozone (O3) concentrations in urban areas is very complex. In addition to the strongly non-linear chemistry of this secondary pollutant, precursors can be released by a variety of sources in different regions, and locally produced O3 is mixed with that transported from the regional or continental scales. All of these processes depend also on the specific meteorological conditions and topography of the study area. Consequently, high-resolution comprehensive modeling tools are needed to understand the drivers of photochemical pollution and to assess the potential of local strategies to reduce adverse impacts from high tropospheric O3 levels. In this study, we apply the Integrated Source Apportionment Method (ISAM) implemented in the Community Multiscale Air Quality (CMAQ v5.3.2) model to investigate the origin of summertime O3 in the Madrid region (Spain). Consistent with previous studies, our results confirm that O3 levels are dominated by non-local contributions, representing around 70 % of mean values across the region. Nonetheless, precursors emitted by local sources, mainly road traffic, play a more important role during O3 peaks, with contributions as high as 25 ppb. The potential impact of local measures is higher under unfavorable meteorological conditions associated with regional accumulation patterns. These findings suggest that this modeling system may be used in the future to simulate the potential outcomes of specific emission abatement measures to prevent high- O3 episodes in the Madrid metropolitan area. [ABSTRACT FROM AUTHOR]

The spiraling growth of the world's population and unregulated urbanization have resulted in many environmental problems, including poor quality of air, which is associated with a wide range of health issues. Machine learning approaches have been extensively employed to predict air quality, attracting the attention of the scientific community worldwide. Bibliometric studies provide a useful means by which to visualize and analyze published works, helping researchers to make novel scientific contributions by filling existing knowledge gaps in the research. To acquire an in-depth understanding of the topic, this paper presents a bibliometric analysis of all published articles on the use of machine learning networks to predict air quality found in the Web of Science (WoS) search engine from 1992 to 2021. S-curve analysis and social network analysis were used to identify the temporal distribution of articles, productivity by countries/continents, research institutions, and scientific metrics of journal productivity. This study indicated that maximum expansion of the literature witnessed during 2017–2021 (second phase) which represents an expansion or growth stage of machine learning and air quality prediction research. The number of published works increased significantly with 1432 articles accounting for 68.51% of all publications. As a result of the increased interest in machine learning-based prediction tools, the number of articles grew 2.17-fold compared to the 1992–2016 (first phase). In terms of international collaboration impact, Italy emerged as the most successful country (43.44), followed by Greece (31.22) and Spain (23.29). Author keywords analysis was employed to explore and evaluate the emerging research trends on the subject of air quality using machine learning models. Keywords that appear most frequently in this study are 'air pollution', 'air quality', 'machine learning', and 'forecasting'. Citation burst analysis, research productivity analysis, highly influential and highly cited works were also employed to examine various research themes and questions. In this study we also discussed how conventional methods were transformed into machine learning approaches. It is expected that this paper will provide technical guidelines, research priorities, and future opportunities for the precise prediction of air quality and emergency management of air pollution globally. • Interest in air quality prediction through machine learning model is expanding. • China and US were found productive countries in terms of countries collaboration. • Big data, Internet of Things, and Smart city are emerging topic for future research. • Significant difference between citations, funded and non-funded research. • Ensemble modeling is popular in air quality prediction research. [ABSTRACT FROM AUTHOR]

During the central months of the year, southwestern Spain is under strong insolation and weak synoptic forcing, promoting the development of sea breezes and mountain-induced winds and creating recirculations of pollutants. The complex topography of the Southwestern Iberia Peninsula induces the formation of vertical layers, into which the pollutants are injected and subjected to long-distance transport and compensatory subsidence. The characteristics of these highly complex flows have important effects on the pollutant dispersion. Air pollution studies in very complex terrains require high-resolution modelling for resolving the flow dynamics. This paper shows the results obtained from using the MM5-CAMx multiscale-nested air quality model to relate the sensitivity regimes for ozone, nitrogen oxides and volatile organic compounds in an area of high geographical complexity. The article assesses the impact on the hourly and eight-hourly maximum daily ozone concentrations of four reduction strategies during two ozone pollution episodes. This analysis of the ozone response has led to a preliminary evaluation of the effectiveness of the most common control strategies: traffic, industry, mixed traffic and industry, and closure of some of the largest industries (oil and petrochemical refineries). Photochemical indicators show that ozone chemistry in southwestern Spain is strongly sensitive to NO. However, volatile organic compound-sensitive points are found in areas with anthropogenic influence (highways, cities and industrial parks). Our results indicate that reductions in road traffic lead to ozone reductions over large areas, whereas reductions in industrial emissions, despite sometimes showing greater decreases in the maximum hourly and eight-hourly ground-level ozone levels, lead to ozone reductions in a local area only. In the control study case, with the oil refinery and the petrochemical plants closed, decreases in ozone hourly concentrations are up to 40% higher than in the other emission control scenarios studied. This analysis provides an assessment of the effectiveness of different policies for controlling precursor emissions by comparing the modelled results for different scenarios. [ABSTRACT FROM AUTHOR]

This paper assesses the health impact, in terms of the reduction of premature deaths associated with changes in air pollutant exposure, resulting from double-aim strategies for reducing emissions of greenhouse gases and air pollutants from the transport sector for the year 2030 in Spain. The impact on air quality of selected measures for reducing emissions from the transport sector (increased penetration of biofuel and electric car use) was assessed by air quality modeling. The estimation of population exposure to NO2, particulate matter (PM) and O3 allows for estimation of associated mortality and external costs in comparison with the baseline scenario with no measures. The results show that the penetration of the electric vehicle provided the largest benefits, even when the emissions due to the additional electricity demand were considered. [ABSTRACT FROM AUTHOR]

This paper presents the main experimental results of surface ozone concentrations measured at a rural area in Northern Spain from February 2000 to December 2005. Daily and seasonal variation of ozone has been analysed. The peak concentration levels are obtained in the afternoon, mean value around 88 µg m−3, with extreme average values of 59 µg m−3 in January and 113 µg m−3 in July. Small differences are found in the mean and median of the ozone levels from April to August, when spring and summer maxima are observed. Despite the great inter-annual ozone variability obtained, most air quality indicators showed a positive trend. Further analysis of the monthly mean ozone concentrations of the main percentiles have also been performed using a harmonic model. The upward trend was 6.2 ± 1.7 µg m−3 for the 98th percentile. To interpret the main features of the annual cycle and describe the ozone peaks, the influence of meteorological factors is studied. In summer, ozone production is governed by local processes, air temperature being the major controlling factor. However, the origin of the spring maximum is not so well identified. [ABSTRACT FROM AUTHOR]

Abstract??Twenty first century epidemiological publications on urban air pollution are confirming that inhalation of fine, airborne particulate matter (PM) has serious chronic human health effects and is a major cause of premature death worldwide. Recently updated recommendations by WHO identify three ?Interim Targets? for the stepped reduction in PM levels within world cities in the quest to achieve an annual mean Air Quality Guideline (AQG) concentration of 20??g/m3for particles less than 10?microns in size (PM10). In this paper we offer a perspective from Spain, a country with the longest record of reporting pollution data from large numbers of urban traffic sites to a central European database (AIRBASE). We can demonstrate that average annual PM concentrations at urban traffic monitoring stations in many European cities continue to be 50?100% above the WHO AQG, a situation exacerbated by high urban PM2.5/10ratios which indicate a dominance of finer, more deeply inhalable particles potentially more detrimental to health. Given that WHO has estimated in 2000 there were well over 250,000 premature deaths in Europe attributable to PM inhalation, such continuing high urban pollution levels are placing a huge burden on European medical resources. [ABSTRACT FROM AUTHOR]

This paper provides scientific results from a European LIFE project carried out in the Valencian region of Spain during the 2017 to 2018 time frame. In 2018, more than 60,000 tons of pesticides were commercialized in Spain, with approximately 15% destined for Valencian crops. In order to improve the air quality in the agricultural areas of this region, an innovative cropping system based on irrigation was developed and compared to conventional treatments based on hand-spray and turbo application. After applying conventional treatments to five types of crops (citrus, persimmon, nectarine, watermelon, and other stone fruits), a total of 13 active substances were detected in the air. The same active substances were applied to crops using the novel irrigation system, and no pesticide was detected in the air. Moreover, applicator and bystander populations in the region were assessed for their risk of inhalation exposure to pesticides, and no risk was found when either of the techniques, the innovative and the conventional agricultural one, were applied. [ABSTRACT FROM AUTHOR]

Due to the exponential growth of the SARS-CoV-2 pandemic in Spain (2020), the Spanish Government adopted lockdown measures as mitigating strategies to reduce the spread of the pandemic from 14 March. In this paper, we report the results of the change in air quality at two Atlantic Coastal European cities (Northwest Spain) during five lockdown weeks. The temporal evolution of gaseous (nitrogen oxides, comprising NOx, NO, and NO2; sulfur dioxide, SO2; carbon monoxide, CO; and ozone, O3) and particulate matter (PM10; PM2.5; and equivalent black carbon, eBC) pollutants were recorded before (7 February to 13 March 2020) and during the first five lockdown weeks (14 March to 20 April 2020) at seven air quality monitoring stations (urban background, traffic, and industrial) in the cities of A Coruña and Vigo. The influences of the backward trajectories and meteorological parameters on air pollutant concentrations were considered during the studied period. The temporal trends indicate that the concentrations of almost all species steadily decreased during the lockdown period with statistical significance, with respect to the pre-lockdown period. In this context, great reductions were observed for pollutants related mainly to fossil fuel combustion, road traffic, and shipping emissions (−38 to −78% for NO, −22 to −69% for NO2, −26 to −75% for NOx, −3 to −77% for SO2, −21% for CO, −25 to −49% for PM10, −10 to −38% for PM2.5, and −29 to −51% for eBC). Conversely, O3 concentrations increased from +5 to +16%. Finally, pollutant concentration data for 14 March to 20 April of 2020 were compared with those of the previous two years. The results show that the overall air pollutants levels were higher during 2018–2019 than during the lockdown period. [ABSTRACT FROM AUTHOR]

Air pollution affects human health and is one of the main problems in the world, including in coastal cities with industrial seaports. In this sense, the city of Gijón (northern Spain) stands out as one of the 20 Spanish cities with the worst air quality. The study aims to identify outliers in air quality observations near the El Musel seaport, resulting from the emissions of six pollutants over an eight-year period (2014–2021). It compares methods based on the functional data analysis (FDA) approach and vector methods to determine the optimal approach for detecting outliers and supporting air quality control. Our approach involves analyzing air pollutant observations as a set of curves rather than vectors. Therefore, in the FDA approach, curves are constructed to provide the best fit to isolated data points, resulting in a collection of continuous functions. These functions capture the behavior of the data in a continuous domain. Two FDA approach methodologies were used here: the functional bagplot and the high-density region (HDR) boxplot. Finally, outlier detection using the FDA approach was found to be more powerful than the vector methods and the functional bagplot method detected more outliers than the HDR boxplot. [ABSTRACT FROM AUTHOR]

Air quality management is underpinned by continuous measurements of concentrations of target air pollutants in monitoring stations. Although many approaches for optimizing the number and location of air quality monitoring stations are described in the literature, these are usually focused on dense networks. However, there are small and medium-size urban areas that only require one monitoring station but also suffer from severe air pollution. Given that target pollutants are usually measured at the same sampling points; it is necessary to develop a methodology to determine the optimal location of the single station. In this paper, such a methodology is proposed based on maximizing an objective function, that balances between different pollutants measured in the network. The methodology is applied to a set of data available for the city of Cartagena, in southeast Spain. A sensitivity analysis reveals that 2 small areas of the studied city account for 80% of the optimal potential locations, which makes them ideal candidates for setting up the monitoring station. The methodology is easy to implement, robust and supports the decision-making process regarding the siting of fixed sampling sites. Image 1 • Small and medium-size urban areas require one fixed sampling point of air pollutants. • Few studies explore methodologies to locate a sole air quality monitoring station. • A multi-pollutant methodology to select the best location of the station is shown. • The methodology is proven to be robust and underpins air quality management. This paper describes an easy-to-use methodology to find out the optimum location for setting up a single air quality monitoring station, especially useful for small and medium-size urban areas. [ABSTRACT FROM AUTHOR]

The paper shows the results of measurements of carbon dioxide concentrations conducted in the school buildings located in two different climates: Białystok (Poland) and Belmez/Córdoba (Spain). CO 2 concentration in first 45 min met requirements of regulations, but with medium occupation of places in classrooms, then the recommended values were excided. We developed a model for CO 2 level estimation. This model allowed us to simulated the CO 2 concentration for full occupation of classrooms in both countries. In these cases CO 2 concentration would exceed maximum value (1000 ppm) during the first hour. Furthermore required air change rates were calculated: for actual occupation they should be in a range 2.5–5 h −1 to maintain IDA2 and carbon dioxide concentration below 1000 ppm, while in case of full occupation even 6–9 h −1 . Finally, surveys of student satisfaction were performed in situ during the measurements of CO 2 level to obtain the dependence of student comfort perception on this concentration. [ABSTRACT FROM AUTHOR]

The management of the COVID-19 pandemic, which required global lockdowns and mobility restrictions, positively impacted air quality (especially the concentrations of primary pollutants). With the progressive return to normal activity during the last few months of 2020 and 2021, air pollutant concentrations were expected to rise again. In this work, together with meteorological conditions and traffic intensity, the concentrations of NO, NO 2 , O 3 , PM 10 , and SO 2 measured at three different locations (urban, industrial, and rural) in southeast Spain, from January 2016 to December 2021, were analyzed. For the three locations, PM 10 concentrations did not change significantly in 2020 or 2021. SO 2 concentrations did not experience changes at the industrial site in 2020 or 2021, but SO 2 concentrations did slightly increase in five months of 2021 in the urban location (with a maximum monthly average concentration of 11 μ g·m − 3 ). In 2020, NO and NO 2 concentrations were, in general, lower than those measured in previous years (2016–2019). Interestingly, at the urban site (San Basilio), the levels of NO and NO 2 were significantly lower in 2021 than in 2020 despite the higher traffic intensity in 2021. While the concentrations of NO and NO 2 decreased, in VOC-limited areas, O 3 concentrations increased. This was clearly observed at the industrial and the urban sites. Despite the increase, O 3 concentrations were still below the European O 3 target value for the protection of human health. An analysis of new registrations of EVs and HEVs in the latter years and their impact on the vehicle fleet of Murcia and, subsequently, on road emissions was carried out. Regardless of the steep increase in environmentally friendly vehicles in 2021, their share of the vehicle fleet is still negligible; thus, the lower concentrations of NO and NO 2 are likely due to more favorable weather conditions in 2021 than in 2020. [ABSTRACT FROM AUTHOR]

Air pollution is one of the big concerns for smart cities. The problem of applying big data analytics to sampling bias in the context of urban air quality is studied in this paper. A nonparametric estimator that incorporates kernel density estimation is used. When ignoring the biasing weight function, a small-sized simple random sample of the real population is assumed to be additionally observed. The general parameter considered is the mean of a transformation of the random variable of interest. A new bootstrap algorithm is used to approximate the mean squared error of the new estimator. Its minimization leads to an automatic bandwidth selector. The method is applied to a real data set concerning the levels of different pollutants in the urban air of the city of A Coruña (Galicia, NW Spain). Estimations for the mean and the cumulative distribution function of the level of ozone and nitrogen dioxide when the temperature is greater than or equal to 30 ∘ C based on 15 years of biased data are obtained. [ABSTRACT FROM AUTHOR]

Abstract: Benzo(a)pyrene (BaP) is one of the most dangerous PAH due to its high carcinogenic and mutagenic character. Because of this reason, the Directive 2004/107/CE of the European Union establishes a target value of 1 ng/m3 of BaP in the atmosphere. In this paper, the main aim is to estimate the BaP concentrations in the atmosphere by using last generation of air quality dispersion models with the inclusion of the transport, scavenging and deposition processes for the BaP. The degradation of the particulated BaP by the ozone has been considered. The aerosol–gas partitioning phenomenon in the atmosphere is modelled taking into a count that the concentrations in the gas and the aerosol phases. If the pre-existing organic aerosol concentrations are zero gas/particle equilibrium is established. The model has been validated at local scale with data from a sampling campaign carried out in the area of Zaragoza (Spain) during 12 weeks. [Copyright &y& Elsevier]

Abstract: The CALIOPE high-resolution air quality modeling system (4 km × 4 km, 1 h) estimates particulate matter from two aerosol models, CMAQv4.5 (AERO4) and BSC-DREAM8b. While CMAQv4.5 calculates biogenic, anthropogenic and sea-salt aerosols; BSC-DREAM8b provides hourly estimates of the natural mineral dust contribution from North Africa deserts. This paper presents an evaluation of the CALIOPE system to reproduce the spatial and temporal variability levels of PM2.5, PM10 and chemical composition (nitrate, non-marine sulfate, ammonium, organic and elemental carbon, sea-salt, and desert dust) across Spain. The evaluation is performed against ground-based observations for the year 2004, when a number of time series of chemically speciated compounds were available. A new data set of Saharan dust PM10 concentration is used to evaluate the PM10 contribution modeled by BSC-DREAM8b. The results indicate that both natural aerosol sea-salt and desert dust accomplish the model performance criteria (MFE ≤ 75% and MFB ± 60%). Modeled PM10 sea-salt is highly dependent on wind speed and presents high correlation with experimental data in coastal areas (r = 0.67). The BSC-DREAM8b is able to reproduce the daily variability of the observed levels of desert dust and most of the outbreaks affecting southern Spain. Species in the equilibrium (e.g. sulfate/nitrate/ammonium) are highly correlated each other and show high dependency on ammonia emissions. Non-marine sulfate and ammonium are underestimated by a factor of 3. An underestimation of nitrate was also seen (factor of 2). Fine carbonaceous aerosols present the highest underestimations (factor of 4) in part related to the state-of-the-science concerning secondary organic aerosol formation pathways. Spatial and seasonal variability of PM2.5, PM10 and their chemical compounds increase the correlation with observations when multiplicative bias-correction factors for the aforementioned underestimated species are taking into account. Furthermore, simulated spatial and seasonal patterns of aerosol agree with those described in related studies based on experimental values. [ABSTRACT FROM AUTHOR]

Abstract: This paper quantifies the very short-term variability of the total solar irradiance and the ultraviolet erythemal irradiance (UVER) averaged over 1-min intervals at Granada (Southeastern Spain). A statistical analysis for a four-year period (January 2006–December 2009) under different cloudiness and characterized by the amount of cloud cover (oktas) retrieved from an All-Sky Imager located next to the radiometers is presented. Very short-term variability of the total solar irradiance was larger than UVER fluctuations under cloudy conditions (above three oktas), in accordance with previous works found in the literature. Nevertheless, for cloud cover bellow three oktas the opposite was true; the median relative 1-min fluctuation was larger for UVER than for total solar irradiance. Moreover, while the coefficient of variation (CV) for UVER presented a clear dependence on the solar zenith angle (SZA) under completely cloud-free conditions (from 1.5% for SZA = 20° to 9.5% for SZA = 65°), the CV of the total solar irradiance was under 1.3% with a more stable behaviour for the entire range of SZA. Large differences were found for cloud cover of seven oktas, where the median diurnal 1-min variability for total solar irradiance was 3.9% min−1 compared to 2.5% min−1 for UVER data. Additionally, an episode with surface total solar irradiance higher than its corresponding extraterrestrial value is analyzed. [Copyright &y& Elsevier]

Abstract: This paper presents a summary of the results obtained from the particulate matter (PM) data collected over the 1999–2008 period from representative sites of the Madrid air basin (an urban traffic site, an urban background site and a rural site) which is located in the centre of Spain. A number of PM10 and PM2.5 filters were obtained with high volume samplers and cut-off inlets and chemically analyzed. Pollutants recorded in this area at the Air Quality monitoring stations were also analyzed. The influence of the atmospheric transport scenarios on the levels of PM was investigated by means of atmospheric back-trajectories, satellite imagery and meteorological synoptic charts. Source apportionment studies were performed to characterize the main PM sources whenever was possible. Their contributions to bulk PM levels showed clear spatial patterns, linked to characteristic meteorological scenarios. [Copyright &y& Elsevier]

Abstract: Damage caused to objects of cultural heritage is one of the most serious detrimental effects of air pollutants. Within the 6th Framework Programme of the EU, the CULT-STRAT project is an inter-agency, multi-disciplinary project that has integrated research and monitoring information to provide answers to policy and management questions, which relate the effects of air pollutants on heritage and the management options available to mitigate them. The overall aim of the project has been to assess and predict the effects of different pollutants on materials and objects of cultural heritage in a multipollutant scenario and to identify indicators and thresholds levels of pollutants. These have been used for development of management strategies for sustainable maintenance and preventive conservation of European Cultural Heritage and air quality policy from continental to local scale. The present paper reports one of the studies carried out in the CULT-STRAT project at city level and focused on the town of Madrid (Spain). Different maps are shown for the present and possible future scenarios (2010 and 2020): inventory of stock of cultural heritage for each selected material, concentration of selected pollutants (SO2, NO2, O3 and PM10), corrosion (cast bronze) and recession (Portland limestone), exceedance of tolerable degradation thresholds for each material and corrosion-cultural heritage overlapped maps. [Copyright &y& Elsevier]

Global warming and air quality are headline environmental issues of our time and policy must preempt negative international effects with forward-looking strategies. As part of the revision of the European National Emission Ceilings Directive, atmospheric emission projections for European Union countries are being calculated. These projections are useful to drive European air quality analyses and to support wide-scale decision-making. However, when evaluating specific policies and measures at sectoral level, a more detailed approach is needed. This paper presents an original methodology to evaluate emission projections. Emission projections are calculated for each emitting activity that has emissions under three scenarios: without measures (business as usual), with measures (baseline) and with additional measures (target). The methodology developed allows the estimation of highly disaggregated multi-pollutant, consistent emissions for a whole country or region. In order to assure consistency with past emissions included in atmospheric emission inventories and coherence among the individual activities, the consistent emission projection (CEP) model incorporates harmonization and integration criteria as well as quality assurance/quality check (QA/QC) procedures. This study includes a sensitivity analysis as a first approach to uncertainty evaluation. The aim of the model presented in this contribution is to support decision-making process through the assessment of future emission scenarios taking into account the effect of different detailed technical and non-technical measures and it may also constitute the basis for air quality modelling. The system is designed to produce the information and formats related to international reporting requirements and it allows performing a comparison of national results with lower resolution models such as RAINS/GAINS. The methodology has been successfully applied and tested to evaluate Spanish emission projections up to 2020 for 26 pollutants but the methodology could be adopted for any particular region for different purposes, especially for European countries. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

This paper provides insights into the potential of cold-ironing for the reduction of externalities. External cost derived from the emissions of CO 2 , NO x , SO x , and PM from berthed ships in the Spanish port system during 2016 are estimated providing a Spain-wide empirical evidence into where the highest externalities exist and where, on a port by port level, the introduction of cold ironing could yield the highest potential on reducing said externalities. The combined overall external costs from both local and global effects of shipping emissions from berthed vessels were between 326 and 440 million Euro. Eco-efficiency parameters are also obtained. It is found that the population in the port city as well as the composition of traffic are key factors when the external costs are determined, and they should be considered when the investment decision about where cold ironing should be placed is taken. • Local and global external costs of 46 Spanish ports are estimated to be 439.700.000€. • Population and traffic mix are key factors for external costs of berthed vessels. • Eco-efficiency indicators can support new policy measures in ports. • The potential of cold-ironing for the reduction of externalities are provided. [ABSTRACT FROM AUTHOR]

The human effect on air quality in cities and the evolution of air pollution is obvious, due to economic activity, vehicle traffic, etc. The situation created indirectly by COVID-19, has caused many countries to impose during certain periods restriction of movement and stoppage of economic activities, which has allowed us to observe the instant effect that occurs on the air quality in cities. This article discusses what the observed effect has been, focusing on the early moments of the pandemic (January 2020 to March 2020), with an analysis of the situation from its origin in China to its arrival in Europe and more specifically the situation created in Spain. After the analysis of the situation, it can be seen the large reduction of pollutants in the air of different cities, and in particular in Spain, which came to reduce about 80%. All this leads us to the observation of how human activity can greatly influence air pollution. [ABSTRACT FROM AUTHOR]

• Temperature and stagnation increased and precipitation less frequent, mainly in warmer months. • Strong variability of weather effects for different regions, seasons and pollutants. • Regional weather penalties up to 0.883 μg⋅m−3⋅yr−1 (95% CI: 0.834, 1.000) for PM 10. • 3220 deaths (CI: 626, 5813) due to weather penalties on PM 10 in 25 years. • 10% greater air quality-related health benefits if weather had remained constant. 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 (C 6 H 6 , CO, NO 2 , NO x , O 3 , PM 10 , PM 2.5 , and SO 2) 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 O 3. 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 PM 10 , 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. [ABSTRACT FROM AUTHOR]

It is well established that in Europe, high O3 concentrations are most pronounced in southern/Mediterranean countries due to the more favourable climatological conditions for its formation. However, the contribution of the different sources of precursors to O3 formation within each country relative to the imported (regional and hemispheric) O3 is poorly quantified. This lack of quantitative knowledge prevents local authorities from effectively designing plans that reduce the exceedances of the O3 target value set by the European air quality directive. O3 source attribution is a challenge because the concentration at each location and time results not only from local biogenic and anthropogenic precursors, but also from the transport of O3 and precursors from neighbouring regions, O3 regional and hemispheric transport and stratospheric O3 injections. The main goal of this study is to provide a first quantitative estimation of the contribution of the main anthropogenic activity sectors to peak O3 events in Spain relative to the contribution of imported (regional and hemispheric) O3. We also assess the potential of our source apportionment method to improve O3 modelling. Our study applies and thoroughly evaluates a countrywide O3 source apportionment method implemented in the CALIOPE air quality forecast system for Spain at high resolution (4 × 4 km 2) over a 10-day period characterized by typical summer conditions in the Iberian Peninsula (IP). The method tags both O3 and its gas precursor emissions from source sectors within one simulation, and each tagged species is subject to the typical physico-chemical processes (advection, vertical mixing, deposition, emission and chemistry) as the actual conditions remain unperturbed. We quantify the individual contributions of the largest NOx local sources to high O3 concentrations compared with the contribution of imported O3. We show, for the first time, that imported O3 is the largest input to the ground-level O3 concentration in the IP, accounting for 46 %–68 % of the daily mean O3 concentration during exceedances of the European target value. The hourly imported O3 increases during typical northwestern advections (70 %–90 %, 60–80 µ g m -3), and decreases during typical stagnant conditions (30 %–40 %, 30–60 µ g m -3) due to the local NO titration. During stagnant conditions, the local anthropogenic precursors control the O3 peaks in areas downwind of the main urban and industrial regions (up to 40 % in hourly peaks). We also show that ground-level O3 concentrations are strongly affected by vertical mixing of O3 -rich layers present in the free troposphere, which result from local/regional layering and accumulation, and continental/hemispheric transport. Indeed, vertical mixing largely explains the presence of imported O3 at ground level in the IP. Our results demonstrate the need for detailed quantification of the local and remote contributions to high O3 concentrations for local O3 management, and show O3 source apportionment to be an essential analysis prior to the design of O3 mitigation plans in any non-attainment area. Achieving the European O3 objectives in southern Europe requires not only ad hoc local actions but also decided national and European-wide strategies. [ABSTRACT FROM AUTHOR]

Annual trends of a group of 66 volatile organic compounds (VOCs), containing 20 ozone precursors, were the aim of a sampling campaign carried out for a year in air at urban and industrial areas from Tarragona region. VOCs were determined by active collection on multisorbent tubes, followed by thermal desorption and gas chromatography–mass spectrometry. The analytical method was developed and validated, showing good levels of detection and quantification, recoveries, precision, and linearity for all the compounds in the range being studied. All the industrial and urban samples taken during the sampling campaign were similar in their qualitative composition. The most abundant compound in all urban and industrial sites was i-pentane, with concentrations between 15.2 and 202.1 μg m − 3 in urban sites and between 1.3 and 98.6 μg m − 3 in industrial sites. In urban sites, the following compounds in order of abundance were toluene, n-pentane, m, p-xylene, and o-xylene, with maximum levels of 150.6, 45.8, 42.3, and 31.7 μg m − 3, respectively. In industrial sites, the most abundant compounds depended on the sampled site. [ABSTRACT FROM AUTHOR]

The ‘Campo de Gibraltar’ region is a very industrialized area where very few air pollution studies have been carried out. Up to date, no model has been developed in order to predict air pollutant levels in the different towns spread in the region. Carbon monoxide (CO), Sulphur dioxide (SO2) and suspended particulate matter (SPM) series have been investigated (years 1999–2000–2001). Multilayer perceptron models (MLPs) with backpropagation learning rule have been used. A resampling strategy with two-fold crossvalidation allowed the statistical comparison of the different models considered in this study. Artificial neural networks (ANN) models were compared with Persistence and ARIMA models and also with models based on standard Multiple Linear Regression (MLR) over test sets with data that had not been used in the training stage. The models based on ANNs showed better capability of generalization than those based on MLR. The designed procedure of random resampling permits an adequate and robust multiple comparison of the tested models. Principal component analysis (PCA) is used to reduce the dimensionality of data and to transform exogenous variables into significant and independent components. Short-term predictions were better than medium-term predictions in the case of CO and SO2 series. Conversely, medium-term predictions were better in the case of SPM concentrations. The predictions are significantly promising (e.g., d SPM 24-ahead = 0.906, d CO 1-ahead = 0.891, d SO2 1-ahead = 0.851). [ABSTRACT FROM AUTHOR]

A complete statistical analysis of meteorological and air pollution data was carried out in the ‘Campo de Gibraltar’ region (in the South of Spain) from 1999 to 2002. This is a heavy industrialized area where, up to date, very few air pollution studies have been made. The main objectives of the study presented here have been the characterization of the meteorological and (gaseous and particulate) air pollution conditions in the area, and the relations between them. Multivariate statistical techniques, such as Principal Component Analysis (PCA), have been applied to the data. The results show that air quality in the area is highly dependent on meteorological conditions such as wind persistence and direction, dispersion capability of the atmosphere, and humidity content. On average, sulphur dioxide and nitrogen oxide air pollution, mainly caused by fuel-oil combustion and traffic, respectively, is not very high. However, an important number of exceedences of the limits established by the EU Directive 1999 for PM10 (particulate matter with a diameter less than 10 μm) have been observed in some points of the area. A significant percentage of these exceedences (about 22% on average) are likely caused by African dust intrusions, which usually take place from May to August. From gaseous and particulate air correlations, it seems that anthropogenic activities contribute with about 19% on average. [ABSTRACT FROM AUTHOR]

Barcelona is one of the most polluted cities in Western Europe, although our levels of air pollution are within the World Health Organisation air quality guidelines. However, high concentrations of air pollution have not been studied yet. Ground ozone levels is a topic of considerable environmental concern, since excessive level of ozone are taken as indicative of high pollution. In terms of the air quality guidelines ozone levels higher than 100µg m-3 can start to be health-hazards for human health. Our objective is to report a detailed analysis of ozone data exceeding the thresholds established by the air quality guidelines. Data analysed were collected in two measurement stations in Barcelona, for the reference period 1991-1996. Applying statistical techniques commonly used in the analysis of extreme values, mainly the Peak Over Threshold method was used for in this study. The analysis reveal that the ozone threshold values for the protection of human health has exceeded many times in both stations. The estimated return values for 3, 10, and 40 yr exceed the threshold value for information to the public of almost once in both stations, also it seems to be unlikely that the threshold value for warning to the public will be exceeded in 40 yr. [ABSTRACT FROM AUTHOR]

Good air quality is essential for both human beings and the environment in general. The three most harmful air pollutants are nitrogen dioxide (NO2), ozone (O3) and particulate matter. Due to the high cost of monitoring stations, few examples of this type of infrastructure exist, and the use of low-cost sensors could help in air quality monitoring. The cost of metal-oxide sensors (MOS) is usually below EUR 10 and they maintain small dimensions, but their use in air quality monitoring is only valid through an exhaustive calibration process and subsequent precision analysis. We present an on-field calibration technique, based on the least squares method, to fit regression models for low-cost MOS sensors, one that has two main advantages: it can be easily applied by non-expert operators, and it can be used even with only a small amount of calibration data. In addition, the proposed method is adaptive, and the calibration can be refined as more data becomes available. We apply and evaluate the technique with a real dataset from a particular area in the south of Spain (Granada city). The evaluation results show that, despite the simplicity of the technique and the low quantity of data, the accuracy obtained with the low-cost MOS sensors is high enough to be used for air quality monitoring. [ABSTRACT FROM AUTHOR]

Heat waves pose additional risks to urban spaces because of the additional heat provided by urban heat islands (UHIs) as well as poorer air quality. Our study focuses on the analysis of UHIs, human thermal comfort, and air quality for the city of Madrid, Spain during heat waves. Heat wave periods are defined using the long-term records from the urban station Madrid-Retiro. Two types of UHI were studied: the canopy layer UHI (CLUHI) was evaluated using air temperature time-series from five meteorological stations; the surface UHI (SUHI) was derived from land surface temperature (LST) images from MODIS (Moderate Resolution Imaging Spectroradiometer) products. To assess human thermal comfort, the Physiological Equivalent Temperature (PET) index was applied. Air quality was analyzed from the records of two air quality networks. More frequent and longer heat waves have been observed since 1980; the nocturnal CLUHI and both the diurnal and nocturnal SUHI experience an intensification, which have led to an increasing number of tropical nights. Conversely, thermal stress is extreme by day in the city due to the lack of cooling by winds. Finally, air quality during heat waves deteriorates because of the higher than normal amount of particles arriving from Northern Africa. [ABSTRACT FROM AUTHOR]