Unexpected long-range transport of glyoxal and formaldehyde
observed from the Copernicus Sentinel-5 Precursor satellite during
the 2018 Canadian wildfires

Alvarado, Leonardo M. A.; Vrekoussis, Mihalis; Richter, Andreas; Hilboll, Andreas; Hedegaard, Anna B. Kalisz; Schneising, Oliver; Burrows, J. P.

doi:10.5194/acp-2019-684

Cited by 13 publications

(29 citation statements)

References 31 publications

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“…(2015). The negative model bias at night, when primary sources dominate, may be an artifact of model resolution, or unresolved long‐range transport (e.g., Alvarado et al, 2020). We find mean errors in model‐simulated HCHO are smaller on a daily basis than for the OMI overpass time alone.…”

Section: Discussionmentioning

confidence: 99%

“…(2019) investigated both modeled and satellite‐based relationships between HCHO and organic aerosols. Satellite‐based HCHO has also been used to derive column hydroxyl radical amounts in the remote troposphere (Wolfe et al., 2019) and to identify long‐range transport of biomass burning plumes over North America (Alvarado et al., 2020). The most direct application of HCHO satellite data to air quality management has been in the area of characterizing ozone production regimes (e.g., Duncan et al., 2010; Jin et al., 2020, 2017; Jin & Holloway, 2015; Martin et al., 2004; Schroeder et al., 2016; Souri et al., 2020; Sun et al., 2018).…”

Section: Introductionmentioning

confidence: 99%

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Satellite Formaldehyde to Support Model Evaluation

et al. 2021

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is an air pollutant affecting allergies and lung function (Mendell, 2007) and cancer risk (Salthammer et al., 2010), leading to its classification as a hazardous air pollutant in the 1990 Clean Air Act, and is included in the National Air Toxics Assessment (NATA) of the United States Environmental Protection Agency (U.S. EPA). While exposure to formaldehyde is greater in indoor air than outdoors (e.g., Daisey et al., 2003; Kinney et al., 2002), ambient (outdoor) concentrations account for about 30% of total exposure (Loh et al., 2007). In addition to its direct health effects, formaldehyde serves an indicator of chemical processes forming ground-level ozone, due to its role as a byproduct of volatile organic compound (VOC) oxidation (Sillman, 1995). As a primary pollutant, HCHO is directly emitted from plants and soils, wildfires and other wood-burnings, mobile vehicle sources, industrial processes, and other combustion and chemical activities (Liteplo et al., 2002; Salthammer, 2013). Secondary production from VOC oxidation exceeds primary emissions in most areas of the U.S. (e.g., D. J. Luecken et al. 2012), with natural VOC sources greater than anthropogenic sources (e.g., Millet et al., 2006). After photolysis and reactions with the hydroxyl radical (OH), formaldehyde has an atmospheric lifetime of a few hours (Atkinson, 2000; Levy, 1972). To support health assessment and regulatory activities, the EPA monitors ambient HCHO across the U.S. However, the data coverage provided by ground-based monitors is limited, with a high level of spatial and temporal heterogeneity. In the summer of 2011, the main focus period for this study, ground-based formaldehyde observations made with a 1, 3, or 24-h duration are available from 132 monitors across the U.S., but most monitors report data for a small fraction of the monitoring period. For example, 85% of monitors only report every 6-12 days.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Satellite Formaldehyde to Support Model Evaluation

et al. 2021

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“…The glyoxal algorithm presented here largely inherits from past developments for predecessor nadir-viewing satellite sensors (Alvarado et al, 2014(Alvarado et al, , 2020aChan Miller et al, 2014;Lerot et al, 2010;Vrekoussis et al, 2009;Wittrock et al, 2006). Figure 1 illustrates for 1 full day of TROPOMI data the resulting main output of every algorithmic component, which we further describe in the following sections, with emphasis on their specificities.…”

Section: Description Of the Algorithmmentioning

confidence: 99%

“…With an enhanced spatial resolution resulting in a number of observations more than 10 times larger than provided by its predecessor OMI, the TROPOspheric Monitoring Instrument (TROPOMI), operating since 2017, allows for observing weak atmospheric absorbers with an unprecedented level of spatio-temporal detail. This has been illustrated by Alvarado et al (2020a), who investigated the large amounts of formaldehyde and glyoxal emitted by the intense North American wildfires in August 2018 as observed by TROPOMI for several days and over long distances. Theys et al (2020) have evaluated the respective contributions to the hydroxyl radical production in fresh fire plumes from nitrous acid, VOCs and other sources with the support of different TROPOMI data sets, including the glyoxal data product described here.…”

Section: Introductionmentioning

confidence: 98%

Glyoxal tropospheric column retrievals from TROPOMI – multi-satellite intercomparison and ground-based validation

et al. 2021

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Abstract. We present the first global glyoxal (CHOCHO) tropospheric column product derived from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite. Atmospheric glyoxal results from the oxidation of other non-methane volatile organic compounds (NMVOCs) and from direct emissions caused by combustion processes. Therefore, this product is a useful indicator of VOC emissions. It is generated with an improved version of the BIRA-IASB scientific retrieval algorithm relying on the differential optical absorption spectroscopy (DOAS) approach. Among the algorithmic updates, the DOAS fit now includes corrections to mitigate the impact of spectral misfits caused by scene brightness inhomogeneity and strong NO2 absorption. The product comes along with a full error characterization, which allows for providing random and systematic error estimates for every observation. Systematic errors are typically in the range of 1 ×1014–3 ×1014 molec. cm−2 (∼30 %–70 % in emission regimes) and originate mostly from a priori data uncertainties and spectral interferences with other absorbing species. The latter may be at the origin, at least partly, of an enhanced glyoxal signal over equatorial oceans, and further investigation is needed to mitigate them. Random errors are large (>6×1014 molec. cm−2) but can be reduced by averaging observations in space and/or time. Benefiting from a high signal-to-noise ratio and a large number of small-size observations, TROPOMI provides glyoxal tropospheric column fields with an unprecedented level of detail. Using the same retrieval algorithmic baseline, glyoxal column data sets are also generated from the Ozone Monitoring Instrument (OMI) on Aura and from the Global Ozone Monitoring Experiment-2 (GOME-2) on board Metop-A and Metop-B. Those four data sets are intercompared over large-scale regions worldwide and show a high level of consistency. The satellite glyoxal columns are also compared to glyoxal columns retrieved from ground-based Multi-AXis DOAS (MAX-DOAS) instruments at nine stations in Asia and Europe. In general, the satellite and MAX-DOAS instruments provide consistent glyoxal columns both in terms of absolute values and variability. Correlation coefficients between TROPOMI and MAX-DOAS glyoxal columns range between 0.61 and 0.87. The correlation is only poorer at one mid-latitude station, where satellite data appear to be biased low during wintertime. The mean absolute glyoxal columns from satellite and MAX-DOAS generally agree well for low/moderate columns with differences of less than 1×1014 molec. cm−2. A larger bias is identified at two sites where the MAX-DOAS columns are very large. Despite this systematic bias, the consistency of the satellite and MAX-DOAS glyoxal seasonal variability is high.

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“…法获取大气边界层内 VOCs 物种浓度的垂直分布数据 [39,40] 。随着民用无人机产业的快速发展，许多性能优异(载荷大、续航时间长和操控稳定度高等)且价格低廉的中小型无人机被广泛用于大气监测领域，获取边界层内各类大气参数的垂直分布数据 [41] 。无人机是近些年来较为热门的大气垂直观测平台，基于无人机平台的各种优异性能，其在大气垂直观测领域有广阔的应用前景。无人机平台的优点在于其机动灵活性较高，能够在复杂环境中使用 [42] ，对后勤保障和观测场地的要求较低，且其使用成本也相对低廉 [43] 。使用电池驱动的无人机可以有效避免燃油废气排放对 VOCs 观测样本可能造成的污染，提高观测数据的可靠性 [44][45][46] 。虽然无人机平台具有较好的操控性和灵活机动性，但也存在诸多因素限制。首先，受当前电池技术发展的限制，无人机的续航时间仍然不足，有效载重较小，并且续航时间随附加设备重量的增加而急剧下降 [47] ，严重限制了一个航次中能够有效获取的观测样本数量；其次，无人机平台主要使用离线分析方法获取 VOCs 物种浓度的垂直分布数据，数据的时空分辨率通常较低 [48] [49][50][51] 。比如， Zhou 等人研发的空气检测传感器 (Kolibri) 就是应用于无人机上的低成本检测传感器，它包括 VOCs 和颗粒物采集器以及黑炭分析仪 [49] 。Chen 等人设计用于无人机平台的苏玛罐采集系统，这种无人机有效载荷 1 kg，能满足 250 m 以下采样需求 [50] 。 1.5 遥感技术遥感技术可以分为卫星遥感和地面遥感，地面遥感技术主要使用激光雷达遥感 (Lidar) 和差分吸收光谱技术(DOAS) 。卫星遥感是借助传感器对远距离环境辐射和反射的电磁波信号进行收集与处理 [52] ；激光雷达遥感是向目标发射激光束，将反射信号与发射信号比较并适当处理，获取 VOCs 浓度的垂直分布数据 [53][54][55] 。遥感技术作为长期稳定的监测手段，能及时反馈地球大气中关键 VOCs 组分浓度的时空分布信息，例如卫星遥感能够能检测到乙二醛和甲醛的垂直柱浓度，并且能够监测全球范围内 VOCs 浓度空间分布的实时变化 [56] 。但由于各类遥感技术的原理不同，空气中的颗粒物和水蒸气等能对遥感信号造成干扰，因此遥感技术的准确性受到限制 [57] ；此外，使用遥感技术测量的 VOCs 种类受到限制，目前遥感技术主要用于对流层大气中甲醛、乙二醛和一些芳香烃的测量 [58,59] 。卫星遥感能长期监测大范围空间内的 VOCs 变化特征 [60] ，常与模型计算结果进行对比分析，以提高模拟结果的准确性 [61] 。比如，借助卫星平台的高空间分辨率遥感数据，研究人员能够有效分析区域燃烧源相关 VOCs 的排放清单并加以验证 [62] ；DOAS 通常用于测量区域甲醛和乙二醛的垂直柱浓度，研究关于它们的大气化学过程 [63] [65] 和人为源(如工厂和机动车排放等) [66] 。源排放类型分为移动源和固定源，移动源(如机动车)影响地面 VOCs 浓度 [67] ；固定源除了影响地面 VOCs 浓度 [68] 外，也影响 VOCs 垂直分布 [69][70][71] 。如 Wada 等人发现富士山上的 VOCs 垂直分布结构受到周边森林排放 VOCs 影响 [69] ；Zhang 等人发现上海机动车排放高浓度的苯，导致地面 200 m 内出现多个峰值 [71] 。当区域地面污染源排放结构存在显著差异时，关键 VOCs 物种和其他污染物(如 NO X 和一氧化碳等)的垂直分布结构也出现显著的空间分布差异…”

Section: 无人机平台无人机平台以无人驾驶飞行器(包括固定翼和多旋翼等)为依托，通常使用离线分析方unclassified