Ultrafine particles and PM2.5 in the air of cities around the world: Are they representative of each other?
Ultrafine particles and PM2.5 in the air of cities around the world: Are they representative of each other?
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DOI：
10.1016/j.envint.2019.05.021
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发表时间：
2019-08-01
影响因子：
11.8
通讯作者：
Morawska, Lidia
中科院分区：
文献类型：
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作者：
de Jesus, Alma Lorelei;Rahman, Md Mahmudur;Morawska, Lidia
像现有的空气质量措施那样，仅仅减少颗粒物的质量，最终能导致超细颗粒物（UFP）的减少吗？本研究的目的是提供更广泛的城市视角，以颗粒数浓度（PNC）和PM2.5（空气动力学直径< 2.5 μ m的颗粒的质量浓度）之间的关系以及影响其浓度的因素。在北美、欧洲、亚洲和澳大利亚10个城市12个月的时间里，每小时的PNC和PM2.5平均值被获取。采用自举法两两比较各城市的均值差和Kolmogorov-Smirnov检验。使用广义加性模型（GAM）获得日和季节趋势。计算了粒子数与质量浓度比和Pearson相关系数，以阐明这两个指标之间关系的本质。结果表明，年平均浓度范围为8.0 × 10.3 ~ 19.5 × 10(3)粒。PNC和PM2.5的分布范围分别为7.0 ~ 65.8 mu g.m（-3）和7.0 ~ 65.8 mu g.m(-3)，数据分布总体偏右，且PNC的分布范围更广。与PM2.5相比，PNC的日变化趋势更为明显，这是由于车辆排放的UFP对PNC的贡献较大。PNC和PM2.5的季节性变化与城市的地理位置和特征有关。基于PNC和PM2.5年浓度中位数的城市聚类结果表明，高PNC水平不会导致高PM2.5，反之亦然。粒子数与质量比（以10(9)个粒子为单位）。Mu g(-1))的取值范围为0.14 ~ 2.2，路边站点的Mu g（-1）为1，城市背景站点的Mu g（-1）为< 1，污染较严重的城市的Mu g值较低。对数变换数据的Pearson’s r范围为0.09 ~ 0.64，表明PNC与PM2.5之间的线性相关性一般较差。因此，PNC和PM2.5测量值不具有相互代表性；而调节PM2.5对减少PNC几乎没有作用。这突出表明，考虑到其潜在的健康风险，有必要制定管理办法和控制措施，以解决特别是在城市地区的超效fp浓度升高的影响。
Can mitigating only particle mass, as the existing air quality measures do, ultimately lead to reduction in ultrafine particles (UFP)? The aim of this study was to provide a broader urban perspective on the relationship between UFP, measured in terms of particle number concentration (PNC) and PM2.5 (mass concentration of particles with aerodynamic diameter < 2.5 mu m) and factors that influence their concentrations. Hourly average PNC and PM2.5 were acquired from 10 cities located in North America, Europe, Asia, and Australia over a 12-month period. A pairwise comparison of the mean difference and the Kolmogorov-Smirnov test with the application of bootstrapping were performed for each city. Diurnal and seasonal trends were obtained using a generalized additive model (GAM). The particle number to mass concentration ratios and the Pearson's correlation coefficient were calculated to elucidate the nature of the relationship between these two metrics.Results show that the annual mean concentrations ranged from 8.0 x 10 3 to 19.5 x 10(3) particles.cm(-3) and from 7.0 to 65.8 mu g.m(-3) for PNC and PM2.5, respectively, with the data distributions generally skewed to the right, and with a wider spread for PNC. PNC showed a more distinct diurnal trend compared with PM2.5, attributed to the high contributions of UFP from vehicular emissions to PNC. The variation in both PNC and PM2.5 due to seasonality is linked to the cities' geographical location and features. Clustering the cities based on annual median concentrations of both PNC and PM2.5 demonstrated that a high PNC level does not lead to a high PM2.5, and vice versa. The particle number-to-mass ratio (in units of 10(9) particles.mu g(-1)) ranged from 0.14 to 2.2, > 1 for roadside sites and < 1 for urban background sites with lower values for more polluted cities. The Pearson's r ranged from 0.09 to 0.64 for the log-transformed data, indicating generally poor linear correlation between PNC and PM2.5. Therefore, PNC and PM2.5 measurements are not representative of each other; and regulating PM2.5 does little to reduce PNC. This highlights the need to establish regulatory approaches and control measures to address the impacts of elevated UFP concentrations, especially in urban areas, considering their potential health risks.