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IMPROVING AIR QUALITY AND HUMAN HEALTH: AN APPROACH BASED ON ARTIFICIAL NEURAL NETWORKS

机译:提高空气质量和人体健康:一种基于人工神经网络的方法

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In 2015 up to 30% of Europeans were living in cities with air pollutant levels exceeding European Union (EU) air quality standards, and around 95% were exposed to high concentrations, namely particulate matter (PM), deemed damaging to health accordingly to the World Health Organization (WHO) Air Quality Guidelines. In order to reduce air pollution effects, particularly in cities where the majority of the population lives, it is important to define effective planning strategies for air quality improvement. For this purpose, the ongoing project LIFE Index-Air aims to develop an innovative and versatile decision support tool for policy makers, based on an integrated modelling approach, from emissions to health effects, which will help to identify measures to improve air quality, reducing PM levels, and quantitatively assess their impact on the health and well-being of the populations. Five European urban areas will be considered, Lisbon (Portugal), Porto (Portugal), Athens (Greece), Kuopio (Finland) and Treviso (Italy) at high spatial and temporal resolution, covering PM_(10), PM_(2.5) and metal elements regulated by EU legislation. For now, the WRF-CAMx air quality modelling system was applied to the Portuguese domains with a spatial resolution of 0.01° (~ 1 km) for 2015. The EMEP emission inventory for 2015 with a spatial resolution of 0.1° and including metal species was considered. For the finest resolution domains (urban) the EMEP emissions were disaggregated to 1x1 km~2, based on spatial proxies and emission sources locations. This paper shows the preliminary air quality modelling results, and presents the methodology, based on Artificial Neural Networks (ANN), which will allow to quickly test different measures to improve air quality and to reduce air pollution effects.
机译:2015年,高达30%的欧洲人居住在欧盟(欧盟)空气质量标准的空气污染物水平的城市中,大约95%暴露于高浓度,即颗粒物(PM),视为对健康有害世界卫生组织(WHO)空气质量指南。为了减少空气污染效应,特别是在大多数人口生命的城市中,重要的是确定空气质量改善的有效规划策略。为此目的,正在进行的项目生命指数 - 空中旨在根据综合建模方法,从排放到健康效果,为决策者开发一个创新和多功能决策支持工具,这将有助于确定提高空气质量,减少空气质量的措施PM水平,并定量评估其对群体的健康和福祉的影响。将考虑五个欧洲城市地区,Lisbon(葡萄牙),波尔图(葡萄牙),雅典(希腊),Kuopio(芬兰)和特雷维索(意大利),以高空间和时间分辨率,覆盖PM_(10),PM_(2.5)和欧盟立法规范的金属元素。目前,WRF-CAMX空气质量建模系统应用于葡萄牙域,2015年的空间分辨率为0.01°(〜1公里)。2015年的EMEP排放库存,空间分辨率为0.1°,包括金属物种经过考虑的。对于最佳解析域(Urban),EMEP排放基于空间代理和排放源位置分解为1x1 km〜2。本文展示了初步空气质量建模结果,并介绍了基于人工神经网络(ANN)的方法,允许快速测试不同措施以提高空气质量,减少空气污染效果。

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