151 research outputs found

    Spatial analysis of CO and PM10 pollutants in Tehran city

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         Nowadays, air pollution in cities with regard to its harmful outcomes has been turned into one of the serious challenges in urban management. Pollutants as Carbon monoxide, sulfur dioxide, and the aerosols that are known to be among the most important factors related to heart, vascular, and lung disease, have underlined public welfare and health, and the organizations concerned with community health undertake remarkable expenses for disease coming out of these pollutants per year. Awareness of the air situation and its quality over periods and the process of air pollutants’ changes in locations, and especially detection of high risk places can play an important and efficient role in urban health management and land use policy-making. In this paper, for the prediction of the possibility of occurring a pollutant in different locations, based on location information, one modern method of analysis entitled indicator kriging method is introduced. Since, nowadays, CO and PM10 are the two major pollutants in Tehran city, using the mentioned method, the probability of occurrence of each of them in Dey 1390 along with their accuracy is being measured and then a map is provided for the possible occurrence of these pollutants over the whole city of Tehran. 

    A Review of 21st-Century Studies

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    PM10 prediction has attracted special legislative and scientific attention due to its harmful effects on human health. Statistical techniques have the potential for high-accuracy PM10 prediction and accordingly, previous studies on statistical methods for temporal, spatial and spatio-temporal prediction of PM10 are reviewed and discussed in this paper. A review of previous studies demonstrates that Support Vector Machines, Artificial Neural Networks and hybrid techniques show promise for suitable temporal PM10 prediction. A review of the spatial predictions of PM10 shows that the LUR (Land Use Regression) approach has been successfully utilized for spatial prediction of PM10 in urban areas. Of the six introduced approaches for spatio-temporal prediction of PM10, only one approach is suitable for high-resolved prediction (Spatial resolution < 100 m; Temporal resolution ¤ 24 h). In this approach, based upon the LUR modeling method, short-term dynamic input variables are employed as explanatory variables alongside typical non-dynamic input variables in a non- linear modeling procedure

    Environmental risk assessment in the mediterranean region using artificial neural networks

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    Los mapas auto-organizados han demostrado ser una herramienta apropiada para la clasificación y visualización de grupos de datos complejos. Redes neuronales, como los mapas auto-organizados (SOM) o las redes difusas ARTMAP (FAM), se utilizan en este estudio para evaluar el impacto medioambiental acumulativo en diferentes medios (aguas subterráneas, aire y salud humana). Los SOMs también se utilizan para generar mapas de concentraciones de contaminantes en aguas subterráneas simulando las técnicas geostadísticas de interpolación como kriging y cokriging. Para evaluar la confiabilidad de las metodologías desarrolladas en esta tesis, se utilizan procedimientos de referencia como puntos de comparación: la metodología DRASTIC para el estudio de vulnerabilidad en aguas subterráneas y el método de interpolación espacio-temporal conocido como Bayesian Maximum Entropy (BME) para el análisis de calidad del aire. Esta tesis contribuye a demostrar las capacidades de las redes neuronales en el desarrollo de nuevas metodologías y modelos que explícitamente permiten evaluar las dimensiones temporales y espaciales de riesgos acumulativos

    Urban Air Pollution Forecasting Using Artificial Intelligence-Based Tools

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    Causal impacts of transport interventions on air quality

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    The transport sector is one of the main sources of air pollution emissions, particularly for carbon monoxide, nitrogen oxides, and particulate matter. Evaluating the effectiveness of transport interventions on improving air quality is essential to informing future policy. However, a comparison of air quality observations before and after an intervention can be biased by various factors, such as weather conditions and seasonality effects. Causal inference methods generally have advantages in intervention evaluation in terms of data requirements, model building, and the interpretation of effect estimates. Causality goes beyond statistical association in the sense that it seeks to measure the net effect of an intervention on an outcome through all possible pathways directing from the intervention to the outcome. Causal inference methods have been applied to address the same question, however, the important confounders (such as weather conditions) are commonly controlled for by including variables in the causal inference model and assuming a parametric relationship. The thesis focuses on understanding the causal impacts of transport interventions on air quality. A novel ex-post policy evaluation framework, combining meteorological normalisation, change point detection, and causal inferencing, is proposed to overcome the limitations of previous approaches, and it is applied to three distinct transport interventions: improving public transport supply (Jubilee Line Extension), tightening road traffic emission standards (London Ultra Low Emission Zone), and restricting both transport activities and supply (COVID-19 lockdown). The Jubilee Line extension led to only small (< 1%) or insignificant changes in air pollution on average in London. The Ultra Low Emission Zone showed an average reduction of less than 3% for NO2 concentrations and insignificant effects on O3 and PM2.5 concentrations. The lockdown reduced the NO2 concentrations in London by less than 12% on average, and it had an insignificant effect on O3, PM10, and PM2.5. Therefore, the empirical results of the thesis consistently highlight the necessity of a multi-faceted set of policies that aim to reduce emissions across sectors with coordination among local, regional, and national government in order to achieve long-term improvements in air quality in cities.Open Acces

    Estimation of Fine Particulate Matter in Taipei Using Landuse Regression and Bayesian Maximum Entropy Methods

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    Fine airborne particulate matter (PM2.5) has adverse effects on human health. Assessing the long-term effects of PM2.5 exposure on human health and ecology is often limited by a lack of reliable PM2.5 measurements. In Taipei, PM2.5 levels were not systematically measured until August, 2005. Due to the popularity of geographic information systems (GIS), the landuse regression method has been widely used in the spatial estimation of PM concentrations. This method accounts for the potential contributing factors of the local environment, such as traffic volume. Geostatistical methods, on other hand, account for the spatiotemporal dependence among the observations of ambient pollutants. This study assesses the performance of the landuse regression model for the spatiotemporal estimation of PM2.5 in the Taipei area. Specifically, this study integrates the landuse regression model with the geostatistical approach within the framework of the Bayesian maximum entropy (BME) method. The resulting epistemic framework can assimilate knowledge bases including: (a) empirical-based spatial trends of PM concentration based on landuse regression, (b) the spatio-temporal dependence among PM observation information, and (c) site-specific PM observations. The proposed approach performs the spatiotemporal estimation of PM2.5 levels in the Taipei area (Taiwan) from 2005–2007

    Modelling PM2.5 with Fuzzy Exponential Membership

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    J Expo Sci Environ Epidemiol

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    Air pollution exposure prediction models can make use of many types of air monitoring data. Fixed location passive samples typically measure concentrations averaged over several days to weeks. Mobile monitoring data can generate near continuous concentration measurements. It is not known whether mobile monitoring data are suitable for generating well-performing exposure prediction models or how they compare with other types of monitoring data in generating exposure models. Measurements from fixed site passive samplers and mobile monitoring platform were made over a 2-week period in Baltimore in the summer and winter months in 2012. Performance of exposure prediction models for long-term nitrogen oxides (NO|) and ozone (O|) concentrations were compared using a state-of-the-art approach for model development based on land use regression (LUR) and geostatistical smoothing. Model performance was evaluated using leave-one-out cross-validation (LOOCV). Models performed well using the mobile peak traffic monitoring data for both NO| and O|, with LOOCV R|s of 0.70 and 0.71, respectively, in the summer, and 0.90 and 0.58, respectively, in the winter. Models using 2-week passive samples for NO| had LOOCV R|s of 0.60 and 0.65 in the summer and winter months, respectively. The passive badge sampling data were not adequate for developing models for O|. Mobile air monitoring data can be used to successfully build well-performing LUR exposure prediction models for NO| and O| and are a better source of data for these models than 2-week passive badge data.U54 OH007544/OH/NIOSH CDC HHSUnited States

    A Review on the Application of Natural Computing in Environmental Informatics

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    Natural computing offers new opportunities to understand, model and analyze the complexity of the physical and human-created environment. This paper examines the application of natural computing in environmental informatics, by investigating related work in this research field. Various nature-inspired techniques are presented, which have been employed to solve different relevant problems. Advantages and disadvantages of these techniques are discussed, together with analysis of how natural computing is generally used in environmental research.Comment: Proc. of EnviroInfo 201
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