In-situ characterization of layered pollution in the wintertime Arctic atmosphere by small sensors

Tjarda Roberts, Meeta Cesler‐Maloney, W. Simpson, Jing-ying Mao, B. Barret, S. Bekki, Brice Temine-Roussel, B. d'Anna, Julia Maillard, F. Ravetta, Jean-Christophe Raut, A. Woods, E. Ioannidis, K. Law
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Abstract

During the Arctic winter, local emissions (e.g. from home-heating, traffic, power station or industry plumes) coupled to poor dispersion caused by strong temperature inversions can lead to severe air pollution events. For example, each winter, Fairbanks (Alaska) experiences high abundances of gaseous pollutants and particulate matter (PM), leading to air-quality exceedances. However, there is still limited knowledge on the coupled physico-chemical and dynamical processes that cause wintertime Arctic pollution and aerosol formation under the very cold and low light conditions, and where levels of oxidants such as ozone at the surface can become depleted under limited vertical mixing. Here, we demonstrate novel deployment of low cost small sensors measuring PM2.5, gases (CO, NO, NO2, O3) and meteorological parameters (P, T, RH) to characterize Arctic atmospheric composition and properties, including mapping vertical distributions. Our three-week pre-ALPACA (Alaskan Layered Pollution and Chemical Analysis) intensive field-campaign took place in downtown Fairbanks in Nov-Dec 2019. Small sensor temperature-dependencies were characterized by instrument cross-comparisons and correction-algorithms developed. Sensors were then deployed near-ground, on the roof of a 19 m building, and on a vertical pulley system set-up along the side of the building for vertical profiling. The small sensors show a strong capability to capture temporal variations in PM2.5, CO, NO and NO2 and O3, across a wide temperature range: surface gas and particle abundances became elevated during a cold-polluted period (temperatures as low as -30 C) and again became elevated during a subsequent warm-polluted period (temperatures around -3 C). Vertical profiling during the warm-polluted period identified strong temperature inversions associated with near-surface layers of high PM2.5 and CO that are distinct from an overlying clean, warm, humid air-mass. During the cold-polluted period, temperature inversions were present but less strong, there was little vertical structure in composition, and PM2.5 was often greater at 20m than at the surface. This finding contrasts with a full winter-season analysis that shows cold surface temperatures typically associated with strong inversions and PM highest at the surface. We invoke plume-rise modelling to show how buoyant plumes from local emissions (e.g. home-heating) can reach heights of about 10-20 m, allowing polluted emissions to rise and accumulate at altitude unless inversions are sufficiently strong to constrain the plume-rise. Causes of the temperature inversions include radiative cooling and advection of overlying warm-air. Our study highlights how small sensor measurements and vertical profiling can help elucidate the coupled processes of atmospheric chemistry, physics, dynamics and emissions that lead to surface air pollution episodes at high latitudes.
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基于小型传感器的北极冬季大气层状污染的原位表征
在北极冬季,当地的排放(例如来自家庭供暖、交通、发电站或工业烟尘)加上强逆温造成的分散不良可能导致严重的空气污染事件。例如,每年冬天,费尔班克斯(阿拉斯加)都会经历大量的气体污染物和颗粒物(PM),导致空气质量超标。然而,对于在极冷和低光照条件下导致冬季北极污染和气溶胶形成的耦合物理化学和动力学过程,以及在有限的垂直混合下表面臭氧等氧化剂水平可能耗尽的情况,我们的知识仍然有限。在这里,我们展示了低成本小型传感器的新部署,测量PM2.5、气体(CO、NO、NO2、O3)和气象参数(P、T、RH),以表征北极大气成分和特性,包括绘制垂直分布。2019年11月至12月,我们在费尔班克斯市中心进行了为期三周的alpaca(阿拉斯加分层污染和化学分析)密集实地调查。较小的传感器温度依赖关系通过仪器交叉比较和开发的校正算法来表征。然后将传感器部署在近地,19米高的建筑物的屋顶上,以及沿着建筑物侧面设置的垂直滑轮系统上,以进行垂直剖面。这些小型传感器显示出强大的能力,可以在很宽的温度范围内捕捉PM2.5、CO、NO、NO2和O3的时间变化:在冷污染时期(温度低至-30℃),地表气体和颗粒丰度升高,在随后的暖污染时期(温度约为-3℃),地表气体和颗粒丰度再次升高。在暖污染时期的垂直剖面分析发现,强逆温与近地表的高PM2.5和CO层有关,这与上覆的清洁、温暖、潮湿气团不同。在冷污染期,逆温现象存在,但逆温强度较弱,组成垂直结构较少,且PM2.5在20m处往往大于地面。这一发现与整个冬季的分析结果形成对比,后者显示地表低温通常与强逆温有关,地表PM最高。我们利用羽流上升模型来展示本地排放(例如家庭供暖)产生的浮力羽流如何能够达到大约10-20米的高度,除非逆温足够强以限制羽流上升,否则污染排放将上升并在高空积聚。温度逆温的原因包括辐射冷却和上覆暖空气的平流。我们的研究强调了小型传感器测量和垂直剖面如何有助于阐明导致高纬度地区地表空气污染事件的大气化学、物理、动力学和排放的耦合过程。
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