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A Two-Stage Method to Estimate the Contribution of Road Traffic to PM 2.5 Concentrations in Beijing, China

机译:估计中国北京道路交通对PM 2.5浓度贡献的两阶段方法

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Background : Fine particulate matters with aerodynamic diameters smaller than 2.5 micrometers (PM 2.5 ) have been a critical environmental problem in China due to the rapid road vehicle growth in recent years. To date, most methods available to estimate traffic contributions to ambient PM 2.5 concentration are often hampered by the need for collecting data on traffic volume, vehicle type and emission profile. Objective : To develop a simplified and indirect method to estimate the contribution of traffic to PM 2.5 concentration in Beijing, China. Methods : Hourly PM 2.5 concentration data, daily meteorological data and geographic information were collected at 35 air quality monitoring (AQM) stations in Beijing between 2013 and 2014. Based on the PM 2.5 concentrations of different AQM station types, a two-stage method comprising a dispersion model and generalized additive mixed model (GAMM) was developed to estimate separately the traffic and non-traffic contributions to daily PM 2.5 concentration. The geographical trend of PM 2.5 concentrations was investigated using generalized linear mixed model. The temporal trend of PM 2.5 and non-linear relationship between PM 2.5 and meteorological conditions were assessed using GAMM. Results : The medians of daily PM 2.5 concentrations during 2013–2014 at 35 AQM stations in Beijing ranged from 40 to 92 μg/m 3 . There was a significant increasing trend of PM 2.5 concentration from north to south. The contributions of road traffic to daily PM 2.5 concentrations ranged from 17.2% to 37.3% with an average 30%. The greatest contribution was found at AQM stations near busy roads. On average, the contribution of road traffic at urban stations was 14% higher than that at rural stations. Conclusions : Traffic emissions account for a substantial share of daily total PM 2.5 concentrations in Beijing. Our two-stage method is a useful and convenient tool in ecological and epidemiological studies to estimate the traffic contribution to PM 2.5 concentrations when there is limited information on vehicle number and types and emission profile.
机译:背景:由于近年来道路车辆的快速发展,空气动力学直径小于2.5微米(PM 2.5)的细颗粒物已成为中国的关键环境问题。迄今为止,大多数可用于估算交通对环境PM 2.5浓度的贡献的方法通常因需要收集有关交通量,车辆类型和排放概况的数据而受到阻碍。目的:建立一种简化的间接方法来估算北京地区交通对PM 2.5浓度的贡献。方法:收集2013年至2014年北京35个空气质量监测站的每小时PM 2.5浓度数据,每日气象数据和地理信息。基于不同AQM站类型的PM 2.5浓度,采用两阶段方法,包括开发了分散模型和广义添加剂混合模型(GAMM)来分别估算交通和非交通对每日PM 2.5浓度的贡献。使用广义线性混合模型研究了PM 2.5浓度的地理趋势。使用GAMM评估了PM 2.5的时间趋势以及PM 2.5与气象条件之间的非线性关系。结果:2013-2014年期间,北京35个AQM站的PM 2.5浓度中位数为40至92μg/ m 3。从北到南,PM 2.5浓度呈显着增加趋势。道路交通对每日PM 2.5浓度的贡献范围从17.2%到37.3%,平均为30%。在繁忙道路附近的AQM站发现了最大的贡献。平均而言,城市车站道路交通的贡献比农村车站高14%。结论:交通排放量在北京每天PM 2.5总浓度中占很大比例。我们的两阶段方法在生态和流行病学研究中是有用且方便的工具,用于在车辆数量,类型和排放特征信息有限的情况下估算交通对PM 2.5浓度的贡献。

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