168 research outputs found

    Particulate Matter Sampling Techniques and Data Modelling Methods

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    Particulate matter with 10 μm or less in diameter (PM10) is known to have adverse effects on human health and the environment. For countries committed to reducing PM10 emissions, it is essential to have models that accurately estimate and predict PM10 concentrations for reporting and monitoring purposes. In this chapter, a broad overview of recent empirical statistical and machine learning techniques for modelling PM10 is presented. This includes the instrumentation used to measure particulate matter, data preprocessing, the selection of explanatory variables and modelling methods. Key features of some PM10 prediction models developed in the last 10 years are described, and current work modelling and predicting PM10 trends in New Zealand—a remote country of islands in the South Pacific Ocean—are examined. In conclusion, the issues and challenges faced when modelling PM10 are discussed and suggestions for future avenues of investigation, which could improve the precision of PM10 prediction and estimation models are presented

    Proceedings of Abstracts 12th International Conference on Air Quality Science and Application

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    © 2020 The Author(s). This an open access work distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.Final Published versio

    Proceedings of Abstracts 10th International Conference on Air Quality Science and Application

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    This 10th International Conference in Air Quality - Science and Application is being held in the elegant and vibrant city of Milan, Italy. Our local hosts are ARIANET and ARPA Lombardia both of whom play a leading role in assessing and managing air pollution in the area. The meeting builds upon the series that began at the University of Hertfordshire, UK in July 1996. Subsequent meetings have been held at the Technical University of Madrid, Spain (1999), Loutraki, Greece (2001), Charles University, Prague, Czech Republic (2003), Valencia, Spain (2005), Cyprus (2007), Istanbul, Turkey (2009) Athens, Greece (2012) and Garmisch-Partenkirchen, Germany (2014). Over the last two decades controls to limit air pollution have increased but the problem of poor air quality persists in all cities of the world. Consequently, the issue of the quality of air that we breathe remains at the forefront of societal concerns and continues to demand the attention of scientists and policy makers to reduce health impacts and to achieve sustainable development. Although urbanisation is growing in terms of population, transport, energy consumption and utilities, science has shown that impact from air pollution in cities is not restricted to local scales but depends on contributions from regional and global scales including interactions with climate change. Despite improvements in technology, users still demand robust management and assessment tools to formulate effective control policies and strategies for reducing the health impact of air pollution. The topics of papers presented at the conference reflect the diversity of scales, processes and interactions affecting air pollution and its impact on health and the environment. As usual, the conference is stimulating cross-fertilisation of ideas and cooperation between the different air pollution science and user communities. In particular, there is greater involvement of city, regional and global air pollution, climate change, users and health communities at the meeting. This international conference brings together scientists, users and policy makers from across the globe to discuss the latest scientific advances in our understanding of air pollution and its impacts on our health and environment. In addition to the scientific advances, the conference will also seek to highlight applications and developments in management strategies and assessment tools for policy and decision makers. This volume presents a collection of abstracts of papers presented at the Conference. The main themes covered in the Conference include: Air quality and impact on regional to global scales Development/application/evaluation of air quality and related models Environmental and health impact resulting from air pollution Measurement of air pollutants and process studies Source apportionment and emission models/inventories Urban meteorology Special session: Air quality impacts of the increasing use of biomass fuels Special session: Air quality management for policy support and decisions Special session: Air pollution meteorology from local to global scales Special session: Climate change and human health Special Session: Modelling and measuring non-exhaust emissions from traffic Special session: Transport related air pollution - PM and its impact on cities and across EuropeFinal Published versio

    Machine Learning for Determining Interactions between Air Pollutants and Environmental Parameters in Three Cities of Iran

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    Air pollution, as one of the most significant environmental challenges, has adversely affected the global economy, human health, and ecosystems. Consequently, comprehensive research is being conducted to provide solutions to air quality management. Recently, it has been demonstrated that environmental parameters, including temperature, relative humidity, wind speed, air pressure, and vegetation, interact with air pollutants, such as particulate matter (PM), NO2, SO2, O3, and CO, contributing to frameworks for forecasting air quality. The objective of the present study is to explore these interactions in three Iranian metropolises of Tehran, Tabriz, and Shiraz from 2015 to 2019 and develop a machine learning-based model to predict daily air pollution. Three distinct assessment criteria were used to assess the proposed XGBoost model, including R squared (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Preliminary results showed that although air pollutants were significantly associated with meteorological factors and vegetation, the formulated model had low accuracy in predicting (R2PM2.5 = 0.36, R2PM10 = 0.27, R2NO2 = 0.46, R2SO2 = 0.41, R2O3 = 0.52, and R2CO = 0.38). Accordingly, future studies should consider more variables, including emission data from manufactories and traffic, as well as sunlight and wind direction. It is also suggested that strategies be applied to minimize the lack of observational data by considering second-and third-order interactions between parameters, increasing the number of simultaneous air pollution and meteorological monitoring stations, as well as hybrid machine learning models based on proximal and satellite data

    Computational Intelligence-based PM2.5 Air Pollution Forecasting

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    Computational intelligence based forecasting approaches proved to be more efficient in real time air pollution forecasting systems than the deterministic ones that are currently applied. Our research main goal is to identify the computational intelligence model that is more proper to real time PM2.5 air pollutant forecasting in urban areas. Starting from the study presented in [27]a, in this paper we first perform a comparative study between the most accurate computational intelligence models that were used for particulate matter (fraction PM2.5) air pollution forecasting: artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS). Based on the obtained experimental results, we make a comprehensive analysis of best ANN architecture identification. The experiments were realized on datasets from the AirBase databases with PM2.5 concentration hourly measurements. The statistical parameters that were computed are mean absolute error, root mean square error, index of agreement and correlation coefficient

    A review of artificial neural network models for ambient air pollution prediction

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    Research activity in the field of air pollution forecasting using artificial neural networks (ANNs) has increased dramatically in recent years. However, the development of ANN models entails levels of uncertainty given the black-box nature of ANNs. In this paper, a protocol by Maier et al. (2010) for ANN model development is presented and applied to assess journal papers dealing with air pollution forecasting using ANN models. The majority of the reviewed works are aimed at the long-term forecasting of outdoor PM10, PM2.5, and oxides of nitrogen, and ozone. The vast majority of the identified works utilised meteorological and source emissions predictors almost exclusively. Furthermore, ad-hoc approaches are found to be predominantly used for determining optimal model predictors, appropriate data subsets and the optimal model structure. Multilayer perceptron and ensemble-type models are predominantly implemented. Overall, the findings highlight the need for developing systematic protocols for developing powerful ANN models
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