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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]

This paper illustrates how Machine Learning techniques can be used to assess the impact of environmental protocols that are sparsely activated over time. A case study is analysed: the impact of a protocol that sets traffic restrictions on NO2 levels in Madrid's urban area. The protocol specifies that these restrictions are active only when NO2 level reaches above a certain threshold. Since the protocol was first enacted, in 2017, restrictions have only been active for 59 days, never longer than ten consecutive days. Cross effects are identified: the protocol magnifies the effect of other relevant features, especially wind speed. Pollution decreases with wind speed. Consequently, episodes of high pollution levels generally occur when wind speed is low. The analysis shows that precisely at these low values of wind speed, an increase in the wind speed has a higher effect on pollution when the protocol is activated than when it is not. The analysis is carried out at a measuring station level, considering eight representative stations. The referred cross effect is clearer at centrally located stations across Madrid. Cross effects of the protocol with other weather features are weaker than that with wind speed. The assessment is based on the computation of Shapley values for classification trees that are built using XGBoost and rolling windows. [ABSTRACT FROM AUTHOR]

Air pollutants harm human health and the environment. Nowadays, deploying an air pollution monitoring network in many urban areas could provide real-time air quality assessment. However, these networks are usually sparsely distributed and the sensor calibration problems that may appear over time lead to missing and wrong measurements. There is an increasing interest in developing air quality modelling methods to minimize measurement errors, predict spatial and temporal air quality, and support more spatially-resolved health effect analysis. This research aims to evaluate the ability of three feed-forward neural network architectures for the spatial prediction of air pollutant concentrations using the measures of an air quality monitoring network. In addition to these architectures, Support Vector Machines and geostatistical methods (Inverse Distance Weighting and Ordinary Kriging) were also implemented to compare the performance of neural network models. The evaluation of the methods was performed using the historical values of seven air pollutants (Nitrogen monoxide, Nitrogen dioxide, Sulphur dioxide, Carbon monoxide, Ozone, and particulate matters with size less than or equal to 2.5 μ m and to 10 μ m) from an urban air quality monitoring network located at the metropolitan area of Madrid (Spain). To assess and compare the predictive ability of the models, three estimation accuracy indicators were calculated: the Root Mean Squared Error, the Mean Absolute Error, and the coefficient of determination. FFNN-based models are superior to geostatistical methods and slightly better than Support Vector Machines for fitting the spatial correlation of air pollutant measurements. [ABSTRACT FROM AUTHOR]

Much work has been done in the context of the hedonic price theory to estimate the impact of air quality on housing prices. Research has employed objective measures of air quality, but only slightly confirms the hedonic theory in the best of cases: the implicit price function relating housing prices to air pollution will, ceteris paribus, be negatively sloped. This paper compares the performance of a spatial Durbin model when using both objective and subjective measures of pollution. On the one hand, we design an Air Pollution Indicator based on measured pollution as the objective measure of pollution. On the other hand, the subjective measure of pollution employed to characterize neighborhoods is the percentage of residents who declare that the neighborhood has serious pollution problems, the percentage being referred to as residents' perception of pollution. For comparison purposes, the empirical part of this research focuses on Madrid (Spain). The study employs a proprietary database containing information about the price and 27 characteristics of 11,796 owner-occupied single family homes. As far as the authors are aware, it is the largest database ever used to analyze the Madrid housing market. The results of the study clearly favor the use of subjective air quality measures. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this work was to investigate the possibility of using several bush and arboreal plant species, usually present as ornamental plants in street and parks, as environmental indicators of pollution. This is a research paper that evaluates the real possibility of using a fast and low-cost procedure to evaluate the pollution degree through data obtained from plant species growing within an urban environment. Methods: Leaves of six different bush and arboreal species were collected from different parts of Madrid (Spain), ranging from highly polluted considered areas to medium and low contaminated ones. A total of 66 chemical elements, from major to minor and trace, were determined for every leaf sample by inductively coupled plasma-mass spectrometry. Statistical analyses were carried out using mainly box and whisker plots, linear discriminant analysis and cluster analysis. Results: The pollution by different elements of the studied areas of Madrid cannot be considered generally dangerous for human health. The level detected for the contaminants, in general, is similar or lower than other urban cities. Pb and V concentrations in plant samples tend to increase as traffic density increases. The different studied plant species showed a different capability of accumulation of certain elements. Cedrus deodara accumulates specially Ag, Hg, Mo and V; Cupressus sempervirens, Zr; Pinus pinea, As and Sb; Nerium oleander Ni, Pb, Mo and Se; Ligustrum ovalifolium, Sc and V; and Pittosporum tobira, Ag, Cd, Rb and Sc. Conclusions: The leaves and needles collected from bush and arboreal plants common in this city have demonstrated to be useful to evaluate the level of pollution not only through the chemical analysis but also through the recognition of the visual injury symptoms. The application of multivariate statistical techniques combined with determining of element concentration and correlation analysis has been proved to be an effective tool for reach the objectives of the present work. This allows visualising quickly the damages and leading the sampling through the points of high-level pollution, saving analysis, time and money. [ABSTRACT FROM AUTHOR]

In many cases, a substantial proportion of large industrial emissions are located in the surrounding areas of cities and are the cause of an important part of air concentrations over the city and surrounding areas. The need to have a tool to analyze and manage these concentrations is the main objective of this contribution. In this paper, we show the implementation of an adapted version of the MM5-CMAQ (Byun et al. , Description of the Models-3 Community Multiscale Air Quality (CMAQ) model. Proceedings of the American Meteorological Society 78th Annual Meeting Phoenix, AZ, Jan. 11–16, 264–268, 1998)—PSU/NCAR and EPA (US) models—modeling system for a large combined cycle power plant located near Madrid city (Spain). The system called TEAP (EUREKA project)—a Tool to Evaluate the Air Quality Impact of Industrial Plants—allows the assessment of the impact of each individual power group (400 MW) in real-time and forecasting mode. As a consequence, the industrial plant and authorities are having a period of time (≈16 h) to decide to switch off one power group or several, to minimize or avoid the possible exceedance of European Union (EU) limits—as declared in the EU directives. The quantification of the impact of these possible exceedances of EU Directives due to emissions produced by the power plant is essential for decision making according to the daily forecasts. We will show the implementation of the TEAP system and operation in two real applications which are operating since summer 2005 and January, 2007 in the surrounding area of Madrid (Spain). [ABSTRACT FROM AUTHOR]

Several studies have illustrated the short-term effects of environmental factors such as air pollution on health, using a wide range of time-series regression models. However, there have been few reports of descriptive analyses based on data visualization, which can be used for a preliminary inspection of relationships between health outcomes and exposure factors. The commonly used scatterplot, displaying a large number of points, cannot adequately assess complex bivariate dependence structures. This paper presents a graphical display, the chi-plot, which, used jointly with the scatterplot, provides a powerful tool to describe a varied range of bivariate dependence structures in environmental epidemiology studies. We studied the relationship between emergency room admissions for asthma, air pollutants and pollen types in Madrid (Spain) during the period 1995–1998. Associations were depicted graphically using chi-plots and were consistent with those previously reported. [ABSTRACT FROM AUTHOR]

Objective: The aim of this paper was to analyse the effects of the urban air pollutants and noise levels on daily emergency hospital admissions of children less than 10 years of age in Madrid. Material and methods: Poisson Regression Models were used to quantify the associations. Meteorological variables, influenza epidemics, pollen concentrations and trends and periodicities were used as controlling variables. Results: The main results obtained were the detected relationship ( p<0.05) between emergency hospital admissions due to organic diseases and noise levels (AR=2.4%) and for PM10 concentrations (AR=2.1%). For respiratory diseases statistically significant associations were detected for noise levels (AR=4.7%) and cold temperatures (AR=3.8%). The main association detected for causes of admissions due to bronchitis was for O3 (AR=36.8%) and for pneumonia was for noise levels (AR=7.7%). Conclusion: The results obtained suggest that noise level particularly is a risk factor for daily emergency hospital admissions for organic and respiratory diseases in children less than 10 years old in Madrid. [ABSTRACT FROM AUTHOR]

The occurrence of local high-pollution episodes in densely populated urban areas, which have huge fleets of vehicles, is currently one of the most worrying problems associated with air pollution worldwide. Such episodes are produced under specific meteorological conditions, which favour the sudden increase of levels of air pollutants. This study has investigated the influence of the mixing layer height (MLH) on the concentration levels of atmospheric pollutants and daily mortality in Madrid, Spain, during the period 2011–2014. It may help to understand the causes and impact of local high-pollution episodes. MLH at midday over Madrid was daily estimated from meteorological radio soundings. Then, days with different MLH over this urban area were characterized by meteorological parameters registered at different levels of an instrumented tower and by composite sea level pressure maps, representing the associated synoptic meteorological scenarios. Next, statistically significant associations between MLH and levels of PM10, PM2.5, NO, NO2, CO and ultra-fine particles number concentrations registered at representative monitoring stations were evaluated. Finally, associations between all-natural cause daily mortality in Madrid, MLH, and air pollutants were estimated using conditional Poisson regression models. The reduction of MLH to values below 482 m above-ground level under strong atmospheric stagnation conditions was accompanied by a statistically significant increase in levels of NO, NO2, CO, PM2.5 and ultra-fine particle number concentrations at urban-traffic and suburban monitoring sites. The decrease of the MLH was also associated to a linear increase of the daily number of exceedances of the UE NO2 hourly limit value (200 μg/m3) and levels of air pollutants at hotspot urban-traffic monitoring stations. Also, a statistically significant association of the MLH with all-natural cause daily mortality was obtained. When the MLH increased by 830 m, the risk of mortality decreased by 2.5% the same day and by 3.3% the next day, when African dust episodic days were excluded. They were also higher in absolute terms than the increases in risk of mortality that were determined for the exposition to any other air pollutant. Our results suggest that when the prediction models foresee values of MLH below 482 m above-ground level in Madrid, the evolution of high-contamination episodes will be very favourable. Therefore, short-term policy measures will have to be implemented to reduce NO, NO2, CO, PM2.5 and ultra-fine particle emissions from anthropogenic sources in this southern European urban location. [ABSTRACT FROM AUTHOR]

The stone traditionally used to build cities contributes to their personality and attests to the geological substrate on which they stand. While stone decay in the built heritage can be attributed to a number of causes, anthropic activity has a particularly significant impact. The geomonumental routes project is one of the initiatives proposed in recent years for urban routes that convey geological fundamentals by observing the rocks present in heritage structures. Its innovative approach addresses traditional stone properties, original quarrying sites and mechanisms of decay. Madrid's Royal Palace is a fine example of the use of traditional building stone in the centre of the Iberian Peninsula. In the geomonumental route proposed, the building doubles as an in situ laboratory that affords an overview of the main petrological properties of the two traditional stones most commonly used in the city's built heritage, the forms of decay they are subject and the factors underlying such alterations. This route constitutes a tool for showing the main petrological features and decay forms in traditional building stones found in urban heritage façades, with a special focus on anthropic impact, primarily air pollution and the use of conservation treatments that time has proven to be unsuitable. [ABSTRACT FROM AUTHOR]