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Context. Problem of traffic prediction in a city is closely connected to the tasks of transportations in a city as well as air pollution detection in a city. Modern prediction models have redundant complexity when used for separate stations, require large number of measuring stations, long measurement period when predictions are made hourly. Therefore, there is a lack of method to overcome these constraints. The object of the study is a city traffic. Objective. The objective of the study is to develop a method for traffic prediction, providing models for traffic quantification at measuring stations in the future under data and resource constraints. Method. The method for agent-oriented traffic prediction under data and resource constraints was proposed in the paper. This method uses biLSTM models with input features, including traffic data obtained from agent, representing target station, and other agents, representing informative city stations. These agents are selected by ensembles of decision trees using Random Forest method. Input time period length is proposed to set using autocorrelation data. Results. Experimental investigation was conducted on traffic data taken in Madrid from 59 measuring stations. Models created by the proposed method had higher prediction accuracy with lower values of MSE, MAE, RMSE and higher informativeness compared to base LSTM models. Conclusions. Obtained models as study results have optimal number of input features compared to the known models, do not require complete system of city stations for all roads. It enables to apply these models under city traffic data and resource constraints. The proposed solutions provide high informativeness of obtained models with practically applicable accuracy level. [ABSTRACT FROM AUTHOR]

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]

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]

Abstract: This paper analyzes the mass size distribution of some inorganic species present in the atmospheric aerosol from a field campaign carried out in Madrid throughout a complete year (February 2007–February 2008). Samplings were performed by means of a micro-orifice uniform deposit impactor (MOUDI). Ambient air was sampled during consecutive nocturnal and diurnal periods, and diurnal/nocturnal behaviors were compared for the twenty night-day sampling pairs that were gathered. Annual and seasonal averages were obtained, and some case studies under specific atmospheric conditions are discussed in the paper. Results have shown that the sulfate and ammonium mass was concentrated in the accumulation mode, between 0.18 and 0.56μm, so that gas-phase and condensation processes for secondary aerosol formation prevailed during the sampling periods in this area. An exception to this behavior was found during a fog event when distributions for these two species were centered in the 0.56–1 and 1–1.8μm size stages, corresponding to the droplet mode. In most of the samples, the ammonium mass measured in these size ranges was enough or almost enough to neutralize inorganic acidity by formation of ammonium sulfate and nitrate. However, a significant sulfate mass not neutralized by ammonium was found in the impactor backup quartz filter (aerodynamic diameter<0.056μm). The concentration of this sulfate and its contribution to the ultrafine fraction mass was higher under good dispersive conditions, prevailing in summer, when particle growth processes are not so favored due to the higher atmospheric dilution factors. The origin of this ultrafine sulfate has been attributed to direct emissions from traffic, associated to the nucleation mode. Regarding the nitrate concentration, it was found higher in the coarse mode than in the accumulation mode on an annual basis. The highest concentrations were measured in winter episodic situations. The marked seasonal variability shown in the accumulation mode by this species is related to the volatility of ammonium nitrate. Among all the studied ions, nitrate also presented the highest differences between the diurnal and nocturnal concentrations in all size ranges. This is probably related to the short time scale required for its formation from gaseous precursors. [Copyright &y& Elsevier]

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]

Air pollution control is one of the main challenges facing modern societies. Consequently, the estimation of population, affluence, and technology impacts on air pollution concentrations (STIRPAT modeling) has become the cornerstone of environmental decision-making. Spatial effects are not usually included in STIRPAT modeling of air pollution. However, space matters: accounting for spatial dependencies significantly improves the accuracy of estimates and forecasts, especially (or only) when dealing with small information units rather than with large ones (countries, large regions, provinces in China, counties and states in the USA, etc.). The latter scale is typical in the literature on air pollution due to the difficulties in finding data on its drivers at a true local scale. Accordingly, this paper has a double objective. The first is the estimation of a spatial panel data STIRPAT model, with the spatial units being both very small and also highly autonomous, developed municipalities. The second is to examine whether an environmental Kuznets curve relationship exists between income per capita and NOx concentrations. A case study has been carried out in the Autonomous Community of Madrid, Spain, at the municipal level. [ABSTRACT FROM AUTHOR]

Traffic flow forecasting is one of the most important use cases related to smart cities. In addition to assisting traffic management authorities, traffic forecasting can help drivers to choose the best path to their destinations. Accurate traffic forecasting is a basic requirement for traffic management. We propose a traffic forecasting approach that utilizes air pollution and atmospheric parameters. Air pollution levels are often associated with traffic intensity, and much work is already available in which air pollution has been predicted using road traffic. However, to the best of our knowledge, an attempt to improve forecasting road traffic using air pollution and atmospheric parameters is not yet available in the literature. In our preliminary experiments, we found out the relation between traffic intensity, air pollution, and atmospheric parameters. Therefore, we believe that addition of air pollutants and atmospheric parameters can improve the traffic forecasting. Our method uses air pollution gases, including C O , N O , N O 2 , N O x , and O 3 . We chose these gases because they are associated with road traffic. Some atmospheric parameters, including pressure, temperature, wind direction, and wind speed have also been considered, as these parameters can play an important role in the dispersion of the above-mentioned gases. Data related to traffic flow, air pollution, and the atmosphere were collected from the open data portal of Madrid, Spain. The long short-term memory (LSTM) recurrent neural network (RNN) was used in this paper to perform traffic forecasting. [ABSTRACT FROM AUTHOR]

Climate change is expected to influence urban living conditions and challenge the ability of cities to adapt and mitigate climate change. This paper describes a new modelling system for climate change impact assessments on urban climate and air quality with feasible computational costs (the expected CPU time is too large for actual supercomputer platforms). The system takes the outputs from a global climate model, which are injected into a dynamical regional climate model (WRF-Chem) with the nested capability activated, with 25 km spatial resolution. In addition, the system uses a diagnostic meteorological model (CALMET) to produce urban detailed information (with 200 m spatial resolution) using this downscaling procedure. At the city level, a simplified chemical-transport model (based on CMAQ and using linear chemistry) is used to map the spatial distribution of the pollutants. The system is applied to five European cities: Madrid, Antwerp, Milan, Helsinki and London (Kensington–Chelsea area). The modelling system was used to simulate the climate and air quality for present year (2011) and future years (2030, 2050 and 2100) using 2011 emissions as control run, because we want to investigate the effects on the global climate on the actual (2011) cities. Effects on temperature, precipitation, and ozone are also considered. We compare the climate and air concentrations in future years 2030, 2050 and 2100 with the control year (2011). Comparison of simulations for present situation (using NNRP reanalysis 2011 data sets) shows acceptable agreement with measurements which give us strong confidence on the results for the RCP IPCC climate future simulations for 4.5 and 8.5 scenarios. [ABSTRACT FROM AUTHOR]

Traditionally, machine learning technologies with the methods and capabilities available, combined with a geospatial dimension, can perform predictive analyzes of air quality with greater accuracy. However, air pollution is influenced by many external factors, one of which has recently been caused by the restrictions applied to curb the relentless advance of COVID-19. These sudden changes in air quality levels can negatively influence current forecasting models. This work compares air pollution forecasts during a pandemic and non-pandemic period under the same conditions. The ConvLSTM algorithm was applied to predict the concentration of nitrogen dioxide using data from the air quality and meteorological stations in Madrid. The proposed model was applied for two scenarios: pandemic (January–June 2020) and non-pandemic (January–June 2019), each with sub-scenarios based on time granularity (1-h, 12-h, 24-h and 48-h) and combination of features. The Root Mean Square Error was taken as the estimation metric, and the results showed that the proposed method outperformed a reference model, and the feature selection technique significantly improved the overall accuracy. [ABSTRACT FROM AUTHOR]

While the COVID-19 pandemic is still in progress, being under the fifth COVID-19 wave in Madrid, over more than one year, Spain experienced a four wave pattern. The transmission of SARS-CoV-2 pathogens in Madrid metropolitan region was investigated from an urban context associated with seasonal variability of climate and air pollution drivers. Based on descriptive statistics and regression methods of in-situ and geospatial daily time series data, this study provides a comparative analysis between COVID-19 waves incidence and mortality cases in Madrid under different air quality and climate conditions. During analyzed period 1 January 2020–1 July 2021, for each of the four COVID-19 waves in Madrid were recorded anomalous anticyclonic synoptic meteorological patterns in the mid-troposphere and favorable stability conditions for COVID-19 disease fast spreading. As airborne microbial temporal pattern is most affected by seasonal changes, this paper found: 1) a significant negative correlation of air temperature, Planetary Boundary Layer height, and surface solar irradiance with daily new COVID-19 incidence and deaths; 2) a similar mutual seasonality with climate variables of the first and the fourth COVID-waves from spring seasons of 2020 and 2021 years. Such information may help the health decision makers and public plan for the future. • Climate factors and air quality can trigger SARS-CoV-2 viral infection transmission. • Synoptic atmospheric circulation patterns are related to COVID-19 waves start-up. • 1st and 4th COVID-19 waves have similar correlations with environmental variables. • COVID-19 and climate driving factors have mutual seasonality patterns in Madrid. • Early health strategies are essential to control the COVID-19 pandemic in Madrid. [ABSTRACT FROM AUTHOR]

Air pollution resulting from massive urban development and increased use of private vehicles is a major environmental concern, with particular relevance in urban areas. Urban public road transport has a significant impact on shaping land use patterns, air pollution and welfare. It must therefore be efficient in terms of air pollution in order to contribute to sustainable metropolitan mobility and economic growth. This study proposes a novel and consistent data envelopment analysis, aiming to identify which urban public transport vehicle is the most efficient in terms of air pollution and therefore environmentally suitable for use in public road transport systems. The case of Madrid has been analyzed, as it is representative of other large cities, which have similar bus alternatives and the common objective of reducing air pollution. Madrid City Council data has been compiled by authors and assessed by a panel of twenty experts to determine the model criteria weights. The results show that the plug-in electric vehicle has the lowest pollutant emission values while delivering the highest performance. Useful recommendations are provided to support public policy decisions related to the complex relationships between urban land use, urban transport and air pollution in urban areas. [ABSTRACT FROM AUTHOR]

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]

All urban areas at risk of breathing polluted air should be identified. In the outskirts of Madrid (Spain), there are roads with high traffic (highway A5) that are <5 meters away from nearby residential homes and schools with children and adolescents. The objective of this study is to ascertain the levels of NO2 in these populated areas. Several NO2 diffusion tubes were installed at a height of 3-m to measure NO2 concentrations in various locations of the A5 during the month of May 2022 (30 days). The four tubes located near the A5 measured a NO2 concentration of 49.7; 88.2; 56.8; and 60 μg/m³. The standard deviation and variation coefficient of the measurements were 0.5 and 2.7%, respectively. According to the WHO (2021), the admissible average annual limit is 10 μg/m³ and the daily limit is 25 μg/m³. This study aimed at measuring the concentration of NO2 near homes and primary and secondary schools located in a "toxic microenvironment" (close to the A5 in Madrid) found high and dangerous levels of NO2 impacting the health of the population. This is an area with a population of low socioeconomic level, which increases the impact on health. [ABSTRACT FROM AUTHOR]

Current European legislation aims to reduce the air pollutants emitted by European countries in the coming years. In this context, this article studies the effects on air quality of the measures considered for 2030 in the Spanish National Air Pollution Control Programme (NAPCP). Three different emission scenarios are investigated: a scenario with the emissions in 2016 and two other scenarios, one with existing measures in the current legislation (WEM2030) and another one considering the additional measures of NAPCP (WAM2030). Previous studies have addressed this issue at a national level, but this study assesses the impact at the street scale in three neighborhoods in Madrid, Spain. NO2 concentrations are modelled at high spatial resolution by means of a methodology based on computational fluid dynamic (CFD) simulations driven by mesoscale meteorological and air quality modelling. Spatial averages of annual mean NO2 concentrations are only estimated to be below 40 µg/m3 in all three neighborhoods for the WAM2030 emission scenarios. However, for two of the three neighborhoods, there are still zones (4–12% of the study areas) where the annual concentration is higher than 40 µg/m3. This highlights the importance of considering microscale simulations to assess the impacts of emission reduction measures on urban air quality. [ABSTRACT FROM AUTHOR]