Heterogeneous chemistry of HONO and surface exchange

Heterogeneous chemistry of HONO and surface exchange A dissertation submitted to the Faculty of Biology, Chemistry and Geoscience at the University of Bayreuth for the degree of Dr. rer. nat. presented by Matthias Sörgel born in Nürnberg Bayreuth, January 2012 Die vorliegende Arbeit wurde in der Zeit von April 2007 bis Januar 2012 an der Forschungsstelle für Atmosphärische Chemie der Universität Bayreuth unter der Betreuung von Herrn Prof. Dr. Cornelius Zetzsch angefertigt. Vollständiger Abdruck der von der Fakultät für Biologie, Chemie und Geowissenschaften der Universität Bayreuth genehmigten Dissertation zur Erlangung des akademischen Grades Doktor der Naturwissenschaften (Dr. rer. nat.) Amtierende Dekanin: Prof. Dr. Beate Lohnert Tag des Einreichens der Dissertation: 11. Januar 2012 Tag des wissenschaftlichen Kolloquiums: 06. August 2012 Prüfungsausschuss: Prof. Dr. Cornelius Zetzsch (Erstgutachter) Prof. Dr. Thomas Foken (Zweitgutachter) Prof. Dr. Andreas Held (Vorsitzender) Prof. Dr. Britta Planer-Friedrich Prof. Dr. Jürgen Senker Drittgutachter: Priv. Doz. Dr. Jörg Kleffmann Summary I Summary Nitrous acid (HONO) is an important precursor of OH radicals, which are the key oxidizing species in the atmosphere and are therefore called the detergent of the atmosphere. Despite the importance of HONO for atmospheric chemistry and about 30 years of detailed research the exact formation mechanisms of both day-and night-time formation remain unclear. The main formation pathways discussed to date are heterogeneous reactions with NO 2 as the HONO precursor or microbiological activity in soil. As the ground surface is a major source of HONO, the vertical distribution of HONO is very sensitive to the extent of vertical mixing. Additionally, some uncertainty in comparing laboratory and field measurements might be caused by the not yet clarified role of relative humidity and surface wetness on HONO formation and deposition, respectively. This study presents field measurements of HONO by fast (~ 5 min time resolution) and sensitive (~ 2 ppt detection limit) long path absorption photometers (LOPAPs). The analysis of the data addresses three major questions: a) Can the HONO daytime source be explained by light-induced NO 2 conversion? b) What is the influence of vertical mixing on HONO mixing ratios, measured simultaneously in and above a forest canopy? c) Can the influence of relative humidity (RH) on HONO mixing ratios be inferred from the field measurements using time series analysis? During the Diel Oxidant Mechanism In relation to Nitrogen Oxides (DOMINO) campaign, HONO and other reactive trace gases were measured above a pine forest in south west Spain. In line with all recently published work, this study also found a substantial daytime formation of HONO. This so called additional daytime source or unknown source was found to be slightly correlated (r² = 0.16) with actinic flux. Normalizing this unknown source to NO 2 mixing ratios improved the correlation (r² = 0.38), which indicates an influence of NO 2 availability. The coefficient of determination improved further to 0.47 by restricting the data to clear days and rejecting data from advection events. Thus, a fraction of the unknown source might be explained by light-induced NO 2 conversion but other factors have to be taken into account. Two processes of light-induced NO 2 conversion, proposed by recent laboratory studies, were shown to be negligible for the semirural conditions during our study. HONO photolysis was found to be the most important primary OH-radical source during DOMINO, contributing 20 % more OH than ozone photolysis integrated over the day. Summary II Vertical exchange of HONO was studied at the “Waldstein-Weidenbrunnen” field site of the University of Bayreuth in the Fichtelgebirge Mountains in south east Germany. The simultaneous HONO measurements in and above a forest canopy highlighted the importance of turbulent exchange for the vertical distribution of HONO mixing ratios. The so-called coupling regimes of the forest (with the air layers above) were found to be a very useful micrometeorological concept to study vertical differences of mixing ratios in a forest. They denote which parts of the forest are coupled to the air layer above the canopy and thus take part in turbulent exchange of energy and matter. With this coupling tool it was possible to explain vertical mixing ratio differences by different sources and sinks and the magnitude of the difference by the intensity of vertical exchange. In order to evaluate the reliability of the vertical differences in HONO mixing ratios measured by two LOPAPs, these instruments have been compared side-by-side under field conditions. The comparison revealed that the LOPAPs agreed within 12 % relative error during dry conditions, but mixing ratios measured under rainy and foggy conditions were ambiguous. Studying the vertical mixing ratio differences of HONO, an unexpected result was that during late morning and around noon they were close to zero. As the lifetime of HONO below canopy of about 250 to 300 min was a factor of 25 to 30 longer than that above canopy of about 10 min, large mixing ratio differences would have been expected. The lack of these differences could be explained by efficient vertical mixing, which was indicated by a full coupling of the forest or a coupling by sweeps and only intermittent decoupling of the subcanopy during these periods. Around sunset, the whole forest became decoupled from the air layers above. This caused a steep increase in mixing ratio differences up to about 170 ppt due to a faster increase below canopy, indicating local formation below the canopy. HONO and RH are correlated due to their diurnal cycles which are mainly caused by radiation. This diurnal contribution has to be removed from the respective signals in order to extract correlations on other timescales. Singular System Analysis, a tool for time series analysis, has been applied successfully to remove diurnal variations and long-term trends from the HONO and RH time series of three different measurement campaigns. Correlations of the higher frequency contributions of the remaining signals were poor but slightly positive. The HONO mixing ratios increase exponentially with RH from about 25 % RH to about 70 % RH. This was not the case for measurements in marine air masses which were possibly influenced by an equilibrium with the sea surface. No clear correlation was found between around 70 and 95 % RH. Above 95 % RH, HONO mixing ratios decreased due to HONO Summary III uptake in droplets and liquid films. These features are in line with previously proposed mechanisms for interactions of water and HONO on surfaces. The study highlighted the need to assess turbulent transport and surface properties in addition to chemistry for understanding the heterogeneous reactions and processes forming HONO. Zusammenfassung IV Zusammenfassung Salpetrige Säure (HONO) ist ein bedeutendes Vorläufermolekül für OH-Radikale. Diese wirken als bedeutendstes Oxidationsmittel in der Atmosphäre und werden deshalb auch Waschmittel der Atmosphäre genannt. Trotz ihrer Bedeutung für die atmosphärische Chemie und nach 30 Jahren intensiver Forschung sind die Bildungsmechanismen der salpetrigen Säure nach wie vor nicht vollständig bekannt. Aktuell werden überwiegend heterogene Reaktionen von NO 2 als HONO-Vorläufersubstanz diskutiert, und zwar sowohl für die Dunkelreaktion als auch für die lichtinduzierten Reaktionen. Als weitere mögliche Quelle wird die HONO-Freisetzung durch Mikroorganismen im Boden diskutiert. Da sich demnach die wichtigsten HONO-Quellen in Bodennähe befinden, ist die vertikale Verteilung von HONO stark von der Effizienz des Vertikaltransports abhängig. Beim Vergleich der in Labormessungen bestimmten HONO-Bildungsraten mit Feldmessungen besteht zudem Unsicherheit durch den möglichen Einfluss der Oberflächenfeuchte, die von der relativen Feuchte abhängt. Die Messungen der salpetrigen Säure wurden mit sogenannten Lang-Pfad-AbsorptionsPhotometern (LOPAP) durchgeführt. Diese Instrumente erlauben verhältnismäßig schnelle Messungen mit ca. 5 min Zeitauflösung und sind gleichzeitig sehr sensitiv (~ 2 ppt Nachweisgrenze). Die Analyse der gewonnenen Daten gliedert sich in drei Hauptbereiche: a) Kann die unbekannte Tagesquelle von HONO mit der lichtinduzierten Reduktion von NO 2 erklärt werden? b) Wie und wie stark beeinflusst die vertikale Durchmischung HONO Messreihen, die gleichzeitig im Wald und über dem Bestand gemessen wurden? c) Kann, unter Verwendung von Methoden der Zeitreihenanalyse, ein Einfluss der relativen Feuchte auf die HONO-Messwerte abgeleitet werden? Bei der Messkampagne „Diel Oxidant Mechanism In relation to Nitrogen Oxides (DOMINO)“ wurden HONO und andere Spurengase über einem Pinienwald in SüdwestSpanien gemessen. In Übereinstimmung mit anderen kürzlich veröffentlichten Studien wurde auch hier eine bedeutende HONO-Tagesquelle gefunden. Es konnte eine schwache Korrelation dieser so genannten zusätzlichen oder unbekannten Quelle mit dem aktinischen Fluss festgestellt werden (r² = 0.16). Normiert man diese unbekannte Quelle auf die gleichzeitig gemessenen NO 2 -Werte, so verbessert sich die Korrelation zum aktinischen Fluss (r² = 0.38), was auf einen Einfluss der NO 2 -Verfügbarkeit hindeutet. Berücksichtigt man nur Sonnentage und schließt gleichzeitig Advektionsereignisse von der Analyse aus so erhält man Zusammenfassung V einen Korrelationskoeffizienten (r²) von 0.47. Daraus lässt sich schließen, dass zumindest ein Teil der HONO-Tagesquelle durch die lichtinduzierte NO 2 -Reduktion erklärbar ist. Jedoch scheinen auch andere Faktoren eine wichtige Rolle zu spielen. Für zwei kürzlich publizierte, aus Labormessungen abgeleitete Mechanismen der lichtinduzierten NO 2 -Umwandlung wurde allerdings kein nennenswerter Beitrag zur HONO-Tagesquelle festgestellt. Dies gilt zumindest für die Bedingungen während dieser Messkampagne, die jedoch auf andere ländliche Gegenden übertragbar sind. Über den Tag integriert war der Beitrag der HONOPhotolyse zur OH-Radikal-Produktion um 20 % größer als derjenige der Ozonphotolyse, und somit verantwortlich für den größten Teil der Primärproduktion an OH-Radikalen. Die Messungen zum Vertikalaustausch von HONO in einem Waldökosystem wurden auf den Messflächen der Universität Bayreuth im Fichtelgebirge („Waldstein-Weidenbrunnen“) durchgeführt. Dieser Teil der Untersuchung unterstreicht die Bedeutung des turbulenten Austauschs für die vertikale Verteilung von HONO. Als äußerst wichtig für die Interpretation der vertikalen Differenzen der HONO-Mischungsverhältnisse erwiesen sich die sogenannten „Kopplungszustände“ des Waldes mit den Luftschichten darüber. Die Bestimmung der Kopplungszustände basiert auf der Detektion von organisierten Strukturen in der Turbulenz, so genannten kohärenten Strukturen. Durch die Betrachtung der Kopplungszustände war es möglich, die vertikalen Differenzen in den HONO-Mischungsverhältnissen, die jeweils über und im Bestand gemessen wurden, durch die Kombination verschiedener Quellen und Senken zu erklären und die Größe der Differenz auf den Vertikaltransport zurückzuführen. Um die Messunsicherheit für die vertikalen Differenzen zu bestimmen, wurden Vergleichsmessungen (side-by-side) mit den beiden LOPAPs im Feld durchgeführt. Unter trockenen Bedingungen waren keine systematischen Abweichungen festzustellen, und die Geräte stimmten innerhalb eines relativen Fehlers von 12 % überein. Bei Nebel und Regen hingegen waren die Abweichungen so groß, dass den Messergebnissen nicht vertraut werden kann. Die Differenzen der HONO-Mischungsverhältnisse vom späten Vormittag bis zum frühen Nachmittag lagen nahe bei null. Auf Grund der immensen Unterschiede der Lebensdauern der HONO Moleküle (~ 10 min über dem Bestand und 250-300 min darunter, durch die Beschattung durch das Kronendach) waren hohe vertikale Differenzen erwartet worden. Die kaum messbaren Unterschiede in den Mischungsverhältnissen konnten mit dem effizienten Vertikalaustausch erklärt werden. Dieser wurde durch die überwiegend vollkomme Kopplung des Waldes mit den darüber liegenden Luftschichten und nur zwischenzeitlicher Entkopplung des Stammraumes angezeigt. Mit der Entkopplung des Synthesis 1 Synthesis 1 Introduction Nitrous acid (HONO) is a key compound to understand tropospheric oxidation chemistry. Its photolysis forms OH radicals which are called the “detergent” of the atmosphere due to their oxidizing power. Most compounds emitted into the atmosphere become more hydrophilic (e.g. NO→HNO 3 ), less volatile (and then are incorporated into the particulate phase) or are finally oxidized to CO 2 and water by this oxidation process. This accelerates the removal of the majority of compounds from the atmosphere by both dry and wet deposition (e.g. Crutzen and Zimmermann, 1991; Ehhalt, 1994). The whole system has been called the “self-cleansing capacity of the atmosphere”. HONO has been found to contribute substantially to primary OH formation close to the Earth’s surface. HONO typically contributes about one third of OH primary production, but published values range from about 10 to 60 % as summarized by Volkamer et al. (2010) and Sörgel et al. (2011b). Besides its importance for the atmospheric oxidation potential, HONO is part of acid and nutrient deposition to the biosphere. Moreover, growing concern exists about possible health effects due to the formation of nitrosamines (Hanst et al., 1977; Pitts et al., 1978) where HONO acts as the nitrosating agent, especially in indoor environments after wall reactions of HONO with nicotine (Sleiman et al., 2010). HONO is believed to be formed heterogeneously, with the main contribution arising from the ground surface (e.g. Wong et al., 2011a). Thus, HONO mixing ratios are very sensitive to vertical mixing. In the planetary boundary layer (PBL), turbulent diffusion is about five orders of magnitude faster than molecular diffusion (Foken, 2008). For example, a compound emitted at the surface (like HONO) would need about a month to be uniformly mixed in the lowermost 10 m by molecular diffusion only, whereas it takes only a few seconds by turbulent diffusion (Jacob, 1999). This has important implications for atmospheric chemistry as diffusion brings reactants which have different sources and sinks together. In 1940, Damköhler introduced a dimensionless number (now named Damköhler number), which compares the characteristic transport timescale to the timescale of a chemical reaction (Damköhler, 1940). Thus, the Damköhler number serves as a measure if a trace gas can be considered a quasi-inert tracer during transport (Da ≤ 0.01). For larger Damköhler numbers (0.01 < Da < 50) transport and chemistry play a role. According to McRae et al. (1982) above Da = 50 the reaction can be Synthesis 2 regarded as diffusion controlled. If the reactants are not well mixed, they are segregated. This means that the effective rate constant is lower than that measured in the laboratory under well mixed conditions. The problem of segregation raised special attention in air chemistry modeling (e.g. Stockwell, 1995; Vila-Guerau de Arellano, 2003; Vinuesa and Vila-Guerau de Arellano, 2005; Ouwersloot et al., 2011). Recent development of fast sensors for reactive species allowed studying the effect of segregation in situ (e.g. Dlugi et al., 2010). Thus, in the real atmosphere a detailed interpretation of the chemistry is not possible without information about the turbulence. This thesis aims to shed light on the distribution of sources and sinks of HONO in forest environments. The identification of sources and sinks is a prerequisite for modeling studies and stimulates new laboratory studies about the nature of these sources and sinks. This work highlights in particular the need to carefully address transport phenomena in deriving source distributions and source strength of reactive species like HONO. 1.1 Atmospheric chemistry of HONO Though HONO is an important compound in the troposphere and has been studied extensively since the unequivocal detection in the atmosphere (Perner and Platt, 1979), the formation pathways are poorly understood. There is a huge body of evidence that the heterogeneous disproportionation of NO 2 to HONO and HNO 3 is the dominant nighttime formation reaction (also called the “dark heterogeneous reaction”). This reaction was found to be first order in NO 2 and water vapor (Sakamaki et al., 1983; Svennson et al., 1987; Pitts et al., 1984; Jenkin et al., 1988). It has been studied on a variety of natural and urban surfaces (Lammel and Cape 1996; Lammel, 1999). Still, the exact mechanism remains unclear. A detailed assessment of the different mechanisms has been given by Finlayson-Pitts et al. (2003) and Finlayson-Pitts (2009). In short: - The gas phase dimer of NO 2 (N 2 O 4 ) dissolves in aqueous films (Finlayson-Pitts et al., 2003) - Chemisorption of water on mineral dust particles produces H, which reacts with NO 2 to form HONO (Gustafsson et al., 2008) - Disproportionation at the droplet surface is anion catalyzed (Yabushita et al., 2009¸ Kinugawa et al., 2011)) Synthesis 3 Another pathway is the reaction of NO 2 with reducing organic compounds (e.g. Gutzwiller et al., 2002a and 2002b). The proposed reactions involving NO (Calvert et al., 1994; AndresHernandez et al., 1996; Saliba et al., 2001) were found to be of minor importance (summarized by Finlayson-Pitts et al., 2003 and Kleffmann, 2007). To date, the mechanism still remains unclear. Nevertheless, the nighttime formation rates of HONO measured in urban and rural environments are within a quite narrow range from 0.4 to 2 % h -1 with respect to NO 2 (summarized by Su et al., 2008a and Sörgel et al., 2011a). The only known relevant gasphase source of HONO is the reaction of NO with OH, which is the back reaction of HONO photolysis that forms NO and OH. During daytime these reactions form a photostationary state (PSS), whereas during nighttime this HONO formation pathway is not important due to the absence of photochemically produced OH (and NO). All recent studies measured daytime HONO values substantially above the PSS which means that an additional daytime source exists (e.g. Kleffmann et al., 2005 and Kleffmann, 2007). This stimulated laboratory studies, which came up with various proposed mechanisms. These can be summarized as follows (a detailed assessment is given in the review of Kleffmann (2007) and in Appendix B and C of this work): - Reduction of NO 2 on organic photosensitizers (e.g. George et al., 2005 and Stemmler et al., 2006) - Photolysis of nitrophenols (Bejan et al., 2006) - Photolysis of adsorbed HNO 3 (e.g. Zhou et al., 2002, 2003 and 2011; Ramazan et al. 2004) - NO 2 reduction on irradiated mineral particles (Gustafsson et al., 2006, Ndour et al., 2008) - Soil emissions from microbiological activity (Su et al., 2011) A promising pathway to explain HONO daytime production are so-called photosensitized reactions (e.g. George et al., 2005), although, these reactions have been demonstrated to play a minor role regarding the HONO formation on organic aerosols (Stemmler et al., 2007; Sosedova et al., 2011). However, as humic acids are ubiquitous in nature, these reactions may substantially contribute to daytime HONO formation on plant or building surfaces as well as soils (Stemmler et al., 2006). Very recent studies about photolysis of adsorbed HNO 3 indeed showed enhanced light absorption of adsorbed HNO 3 with respect to gas phase HNO 3 (Zhu et al., 2008 and 2010). This makes a substantial contribution of this pathway to daytime HONO formation more realistic. As these laboratory studies found NO 2 * as main photolysis product, Synthesis 4 Zhou et al. (2011) concluded that HONO formation by HNO 3 photolysis also follows the photosensitized reduction of NO 2 (Stemmler et al.,2006). HNO 3 is the final oxidation product of NO x (Fig.1) and is thus believed to determine the atmospheric lifetime of NO x . HNO 3 photolysis therefore provides a pathway back to the atmospheric oxidation cycle of NO x (Fig.1). This mechanism is especially important for the oxidation capacity in remote areas with low atmospheric NO x burden. Another “way back” is the proposed HONO emission from soils due to microbiological activity (Kubota and Asami, 1985; Su et al., 2011). The denitrification by microbes is the only pathway to convert reactive nitrogen in the environment back to unreactive N 2 . The loss of intermediate products is responsible for the emissions of HONO and NO (Fig.1). Via nitrification, also fertilization with reduced nitrogen (NH 3 /NH 4+ ) can form reactive oxidized Nspecies (NO and HONO). Fig.1: A schematic view (not complete) of the atmospheric chemistry of reactive oxidized nitrogen and its interaction with the ground surface respectively soil (brown layer). Blue arrows denote pathways which are active during the whole day, black arrows contribute only in the absence of light, and red arrows only with light. Figure 1 presents a schematic view on atmospheric chemistry of oxidized inorganic nitrogen. Anthropogenic and biogenic emissions are mainly in the form of NO. NO is further oxidized in the atmosphere by O 3 and OH to be finally deposited as HNO 3 (e.g. Lerdau et al., 2000). During night, HNO 3 is formed by heterogeneous hydrolysis from N 2 O 5 . N 2 O 5 is formed only at nighttime because it requires the reaction of NO 2 with the NO 3 radical (formed by reaction Synthesis 5 of O 3 with NO 2 ) which is very photolabile. If NO is oxidized to NO 2 other than by O 3 (e.g. by HO 2 , RO 2 radicals), O 3 is formed by this cycle from NO 2 photolysis (Finlayson-Pitts and Pitts, 2000). If HONO is formed by other means than through reaction of NO with OH, OH radicals are formed by HONO photolysis. Thus, oxidized nitrogen has an important impact on the self-cleansing capacity of the atmosphere (day and nighttime). As can be seen in Fig. 1, the formation of HONO is mainly heterogeneous (reactions at surfaces). In principle, these can be both aerosol and ground (building, plant, soil,…) surfaces. There is strong evidence from field measurements that the ground is indeed the major source of HONO (e.g. Febo et al., 1996; Kleffmann et al., 2003; Zhang et al., 2009; Wong et al., 2011a, 2011b). Therefore, HONO mixing ratios are very sensitive to vertical mixing. Summarizing: The formation pathways of HONO remain unclear, although there are quite a lot mechanisms proposed. In the dark, the heterogeneous disproportionation of NO 2 is the most probable source, whereas at daytime an additional light enhanced or photolytic source exists. HONO from microbiological activity may be a source both day and night. According to Su et al. (2011; supporting material) the HONO source strength is dependent inter alia on temperature (HONO equilibrium) and on transfer velocity (from soil to the atmosphere). These parameters exhibit a diurnal cycle which can lead to more efficient HONO transport to the atmosphere during daytime. The most probable HONO sources are located at the ground or in the soil itself. Therefore, as already mentioned above, HONO mixing ratios are very sensitive to vertical mixing. 1.2 HONO chemistry and turbulent transport In rural and remote regions the HONO precursor NO 2 is advected from source regions (e.g. cities, roads) and to some extent locally produced by oxidation of soil-emitted NO which reacts with ozone. The NO 2 has to be transported to the surface where it reacts to form HONO (or is taken up by plants¸ e.g. Lerdau et al., 2000; Breuninger et al., 2011). HONO formed at the surface desorbs and is then transported back to the atmosphere. In stable conditions, upward transport is limited, thus HONO accumulates close to the ground. If HONO is predominantly formed by microbes or by HNO 3 photolysis, NO 2 deposition is of minor importance. During neutral or convective conditions (mostly daytime), vertical gradients are Synthesis 6 less pronounced or hardly resolved by current instrumentation (~ 1 ppt detection limit). This can be seen in the vertical profiles of HONO in the boundary layer above a remote forest measured on a small airplane (Zhang et al., 2009). Thus, especially during stable conditions the measured mixing ratios and also the HONO/NO 2 ratio depend on the measurement height (Stutz et al., 2002; Veitel, 2002; Wong et al., 2011a). Furthermore, the surface properties are altered by the adsorption of water. This has an influence on solubility (deposition) and on chemistry. Water is required as a reactant for the formation of HONO by heterogeneous disproportionation of NO 2 . However, with increasing adsorption of water molecules, surface active sites for other reactants (NO 2 ) might be blocked (Lammel and Cape, 1996). Also, Gustafsson et al. (2006) report an inhibition of photocatalytic HONO formation due to adsorption of water. On the other hand, HONO is a weak acid which is taken up into liquid films depending on pH (Hirokawa et al., 2008). Furthermore, HONO can be salted out (become less soluble) in concentrated solutions, which was found for sulfuric acid (Becker et al., 1996) and ammonium sulfate solutions (Becker et al., 1998). A relation between gas phase HONO and relative humidity (RH) was found in many laboratory and field measurements (e.g. Arens et al., 2002; He et al., 2006;Trick, 2004; Stutz et al., 2004; Wainmann et al., 2001; Wojtal et al., 2010; Yu et al, 2009; Sörgel et al., 2011a; Rubio et al., 2008). In accordance with the formation of liquid films or droplets above 95 % RH (Burkhardt and Eiden, 1994; Lammel, 1999), lower HONO and HONO/NO x values above 95 % RH were reported (Stutz et al., 2004; Yu et al., 2009; Sörgel et al., 2011a). The behavior in the intermediate RH range (~20-95 %) is not well documented. The only mechanism provided so far is a Langmuir type mechanism where co-adsorbing water displaces HONO adsorbed to the surface (Trick, 2004; Stutz, 2005). Up to know it is unclear which role surface humidity plays for tropospheric HONO. A forest canopy adds more complexity as shown in a simplified scheme of daytime NO x and HONO chemistry within and above a forest canopy in Fig. 2. A key feature regarding photochemistry is the shading of the canopy which alters photochemical equilibriums. For example, the photolysis of the HONO precursor NO 2 is faster above canopy, which results in a net downward flux of NO. In the shaded trunk space this downward mixed NO (together with soil emitted NO) reacts with ozone which was also photochemically produced above the canopy to regenerate NO 2 . However, NO 2 is also deposited to the forest floor and the canopy. There it is taken up by plants (e.g. Lerdau et al., 2000; Breuninger et al., 2011) or reacts to form HONO. HONO itself is also deposited to the forest floor and the canopy, where it is taken up by the stomata (Schimang et al., 2006). As discussed earlier, the emission and Synthesis 7 deposition of HONO might depend on RH, which has a distinct vertical gradient and is different within the canopy and at the forest floor. The scheme of NO x chemistry in forest environments is rather well established (Rummel et al., 2002, Horii et al., 2004; Foken et al., 2011). Investigating the HONO sources and sinks in and above the forest canopy was part of this thesis. Additionally to the differences in chemistry the canopy might act like a gate, which separates these two distinct (photochemical) environments. The coupling between the forest canopy and the air layer above (”control mechanism of the gate”) depends on turbulence and might be represented by coupling regimes (Thomas and Foken, 2007). Figure 2: Schematic view of the daytime cycles of NO x and HONO above and below a canopy. Synthesis 8 1.3 Surface exchange As mentioned above, the exchange of energy and matter between the Earth’s surface and the atmosphere is driven by turbulence which is generated by shear and buoyancy forces at the surface. In a first simplified view, a forest canopy can be regarded as a rough wall. Near-wallturbulence has been studied in hydrodynamics since the 1930s and has been found to be comprised of “classical random turbulence” and “organized motions” = “coherent structures” (Robinson, 1991). Similar to wall turbulence coherent motions were found to contribute significantly or even dominate momentum, heat and scalar exchange in tall canopies (Gao et al., 1989; Bergström and Högström, 1989; Barthlott et al., 2007; Thomas and Foken, 2007; Serafimovich et al., 2010). However, the turbulence structure above tall canopies has been found to differ from that of a rough wall. Due to the high roughness, a layer called roughness sublayer (Garratt, 1978, 1980) which extends to about three times the canopy height (e.g. Cellier and Brunet, 1992; Wenzel et al., 1997) lies between the “classical” boundary layer and the canopy. According to Raupach et al. (1996) the turbulence in the roughness sublayer is better characterized by a plane mixing layer than a boundary layer. These authors compare the instabilities which generate turbulence in the mixing of two air streams with different velocities (plain mixing) with those arising from the inflection point in the wind profile within the canopy. This is another difference to rough walls: Below the dense obstacle (canopy) a more open space (trunk space) exists, before, approaching the surface the horizontal wind speed tends to zero. Thus, the mean wind profile of a canopy is different from that of a boundary layer as it has an inflection point (secondary wind maximum). This inflection point is thought to cause instabilities which produce coherent eddies (Raupach et al., 1996; Finnigan, 2000). Thomas and Foken (2007) used the detection of coherent structures to infer so-called “coupling regimes”. The regimes denote which part of the canopy is coupled to the air layer above canopy and thus takes part in the exchange of energy and matter (Thomas and Foken, 2007; Serafimovich et al., 2010). Counter gradient fluxes (Denmead and Bradley, 1985), which violate the flux gradient relationship of classical K-theory, were found to be caused by coherent exchange (Finnigan, 2000). Thus, the classical K-theory is not applicable within a forest canopy. In the roughness sublayer, fluxes are enhanced (with respect to the surface layer), and for the flux gradient relationship correction terms have to be applied (Cellier and Brunet, 1992; Garrett, 1992). Synthesis 9 1.4 Time scales and spatial scales Fig.3: Spatial and temporal scales in the atmosphere adapted from Orlanski (1975). Forest canopy related transport processes (adapted from Foken et al., 2011) comprise turbulent transport in the canopy (black hexagon), vertical advection in the canopy (grey circle), transport above canopy (green triangle), coherent structures (red vertical bar), footprint averaged turbulent flux (pink triangle), and horizontal advection at canopy top (red square).The horizontal bars at the bottom mark chemical timescales for heterogeneous nighttime formation (black horizontal bar) and proposed daytime formation rate from NO 2 (red horizontal bar). The time resolution of the LOPAP instrument is marked as blue diamond. The range of lifetimes of HONO due to photolysis is marked as red (daytime) and black (nighttime) arrow. Spatial scales and time scales of atmospheric motion are closely related (Orlanski, 1975; Fig. 3). Studying the distribution or exchange of trace gases with the surface, one has to be aware that chemistry, biological and soil processes occur on spatial and temporal scales different of those of the related transport in the atmosphere. Thus, measurements of trace gases are not directly related to individual (chemical or physical) processes, but integrated via “volume averaging” or in the case of turbulent measurements over a footprint (so called “scale problem”; e.g. Foken et al., 2011). The discrepancy increases with increasing spatial/temporal scales (Foken et al., 2011). To address the problem of the overlapping (or non-overlapping) Synthesis 10 scales was one of the major goals of the EGER project. In Fig. 3, canopy related transport phenomena (adapted from Foken et al., 2011) are shown in relation to spatial and temporal scales of atmospheric motion according to Orlanski (1975). Not shown here are the soil and biological processes which were a central part of the investigation of the EGER project. This graph is solely focused on chemical reactions governing the formation and fate of HONO as well as the instrumental limitations (temporal resolution) of the LOng Path Absorption Photometer (LOPAP, blue diamond), which are relevant for this thesis. Timescales of heterogeneous HONO formation (day and nighttime) were inferred from typical (rural) NO 2 conversion frequencies and typical HONO/NO 2 ratios. These are given for the dark heterogeneous reaction (black horizontal line) as 1.5 % h -1 and 10 %, respectively (Su et al., 2008a, Sörgel et al., 2011a), and are about 15 % h -1 and 3 %, respectively, for photo-enhanced formation (Sörgel et al., 2011c). An NO 2 value of 1 ppb was taken as a typical rural value. Characteristic timescales for these reactions were calculated by taking the time which the conversion of NO 2 takes at the given rate to reach 63 % of the final HONO/NO 2 ratio. This is similar to the approach used by Dlugi (1993) using the lifetime of a molecule with respect to a certain reaction as a chemical timescale, i.e. the inverse of the reaction rate constant times the concentration of the reaction partner (for bimolecular reactions; τ = [x]k -1 ). If the lifetime of a molecule with respect to this certain reaction is not the limiting lifetime in a transport volume or, like for the NO-NO 2 -O 3 triad, interchange reactions play a role, the approach of Lenschow (1982) is better to use. The LOPAP has a time resolution of 5 - 10 min. According to the scheme of Fig. 3 this means that each data point reflects a spatial integration of several hundred meters (volume averaging). Thus, only larger scale motions can be directly resolved by the LOPAP instrument. Furthermore, chemical timescales for formation (black and red horizontal bar) and loss (intensity of photolysis/ red and black arrow) are of the same magnitude as the timescales of the transport processes resolved by the LOPAP. Therefore, both chemistry and transport have an influence on HONO mixing ratios. Due to the limited lifetime of HONO due to photolysis, measurements during day are more locally influenced (within few km) than during night. Synthesis 17 Figure 5: Side-by-side measurements of the two LOPAP instruments from 27 September (noon) to 3 October 2007 (noon) at the “Waldstein-Weidenbrunnen” research site. Relative differences of the HONO signals (black dots) and visibility range (red squares, dashed lines, maximum range 2000 m). The insert shows the regression obtained during dry conditions (N = 247) from 29 September (14:00 CET) to 2 October (10:00 CET) using standard major axis (SMA) regression. The upper panel shows the mixing ratios measured by the two LOPAP instruments. Missing values are due to zero air measurements and calibration of the LOPAP instruments. Taken from Sörgel et al., 2011a . 4.2 Daytime source During daytime HONO is photolyzed to OH and NO. NO and OH react in a termolecular reaction which regenerates HONO. These reactions reach a photostationary state (e.g. Kleffmann et al., 2005). If the reaction of NO with OH would be the only HONO source during daytime, this cycle would not result in net OH radical formation. All recent studies measured HONO mixing ratios well above the photo stationary state (PSS), although only in few studies all quantities necessary to calculate the PSS were measured directly (summarized by Kleffmann, 2007; Sörgel et al., 2011b). During the DOMINO campaign all required quantities (NO, HONO, OH, j(HONO)) were measured directly, and the measurements were collocated. Measured HONO values were more than a factor of three higher than PSS values for most of the data (75 percentile). As OH measurements were possibly influenced by Synthesis 18 interferences, the calculated PSS values represent rather upper limits. Thus, this study confirmed the existence of an additional daytime source. Furthermore, in this study the source strength of OH radical formation from HONO photolysis was compared to the “classical” primary OH source from ozone photolysis. Although the contribution of ozone photolysis was higher during intense UV insolation around noon, the integrated OH formation over the day was about 20 % higher from HONO photolysis. HONO was the most important primary OH radical source during the DOMINO campaign (Regelin, 2011). The additional daytime source can be calculated by combining known sources and sinks to a budget equation (Su et al., 2008b; Sörgel et al., 2011c). From this budget, the unknown HONO daytime source (P unknown ) was derived, with the assumption dHONO/dt = P(roduction)-L(oss) = 0. The production terms consist of the dark heterogeneous formation (P het ), the reaction of NO with OH (P NO+OH ) and the unknown source (P unknown ) and therefore P = P het + P NO+OH + P unknown . The loss terms are the deposition (L dep ), the photolysis (L phot ) and the reaction of HONO with OH and therefore L = L dep +L phot +L HONO+OH . Thus, the unknown source can be calculated as P unknown = L – (P het +P NO+OH ) + dHONO/dt. Hence, measured increases in concentrations (∆HONO/∆t > 0) mimic source terms, and decreasing concentrations (∆HONO/∆t < 0) mimic sink terms. As ∆HONO/∆t has a substantial contribution to the HONO budget (see Fig. 6) it was further analyzed. Firstly, to exclude additional source or sink terms simply caused by instrument variations, values of ∆HONO/∆t within the instrumental error (± 12 %) of the LOPAP have been omitted. The relative contribution of ∆HONO/∆t to the HONO budget was found to depend on the averaging time with the lowest contribution for 30 min averages as fluctuations are averaged out. Nevertheless, a higher time resolution of 5 min was chosen. Most of the ∆HONO/∆t values larger than the instrumental error of the LOPAP were caused by advection (simultaneous peaks e.g. in NO x , black carbon), where the arrival of the plume mimicked a source term whereas the fading mimicked a sink. Figure 6 shows the mean budget contribution of the different production (P het , P NO+OH ) and loss processes (L dep , L HONO+OH , L phot ). P het is the parameterized “dark heterogeneous” formation, which was parameterized from the nighttime increase of HONO mixing ratios (after Alicke et al., 2002). However, as discussed in detail by Sörgel et al. (2011c), it is questionable if these values are transferable to daytime conditions. This is because HONO is formed heterogeneously, and thus, the formation rate depends not only on the precursor (NO 2 ) concentration but also on the available reactive surface in a given volume (surface to volume ratio; S/V). As the mixed volume (V) depends on vertical diffusivity, the S/V ratio in turn depends on atmospheric stability which is different during the Synthesis 19 day and nighttime. During day, vertical mixing is typically enhanced (neutral or convective surface layer), which increases the mixed volume, and thus the S/V ratio becomes smaller. The gas phase reaction of NO with OH (P NO+OH ) is the most important known formation reaction. The loss of HONO by deposition (L dep ) was parameterized in a simple way by scaling the deposition flux (deposition velocity times concentration) by the mixed layer height (after Harrison et al., 1996). As the loss by deposition occurs at surfaces (ground or aerosol) L dep can also be regarded as a heterogeneous loss reaction which therefore is also sensitive to S/V. A constant mixed layer height of 1000 m was assumed for the parameterization. This may lead to an underestimation of the relative contribution of HONO loss by deposition in a shallow boundary layer, which might explain a “negative unknown source” in the morning and the afternoon (cf. Fig. 6). If wetting of surfaces in the morning and afternoon may be an alternative explanation is ongoing research. Nevertheless, the contribution of L dep to the HONO budget is negligible during most of the day. Furthermore, the loss of HONO by the reaction of HONO with OH (P HONO+OH ) is also almost negligible (< 5 % for all data). The dominating loss term during day is therefore photolysis (P phot ). The most important HONO formation term is P unkown . Figure 6: Contributions of production (bluish colours) and loss terms (hourly means 21st Nov. to 5th Dec.) as well as the unknown daytime HONO source P unknown . Taken from Sörgel et al., 2011c. Synthesis 20 In order to improve the comparability with other studies (urban and remote regions) and to analyze the relation of P unknown to the most probable precursor NO 2 and the actinic flux (lightinduced conversion) Sörgel et al. (2011c) introduced a normalization of P unknown by NO 2 . A very recent study about HONO daytime gradients used the same scaling approach (Wong et al., 2011b). It was shown (Fig. 7; Sörgel et al., 2011c) that the scaling efficiently removed the high HONO formation values caused by advection of polluted air in the morning. The normalization led to a slightly better linear correlation with the photolysis frequency of NO 2 (r² = 0.38 instead of 0.16). Furthermore, it provided evidence for the existence of an upper limit for NO 2 conversion depending on light intensity (Fig. 7b; Sörgel et al., 2011c). The coefficient of determination could be further improved to 0.47 by restricting the data only to clear (dry) days and excluding the values influenced by advection (ΔHONO/Δt > relative error LOPAP, filled red dots Fig. 7). There might be several reasons for this weak correlation. Firstly, there are other local HONO sources like soil emissions (Su et al., 2011) or photolysis of adsorbed HNO 3 (e.g. Zhou et al., 2011) which do not involve direct NO 2 conversion. A hint in that direction might be that the highest conversion frequencies (NO 2 to HONO in % h -1 Fig. 7b) were measured on a quite clean day with low NO x values. As important parameters (surface nitrate loading, content of photosensitizers on the surfaces, HONO soil emissions and vertical diffusivity) to quantify the source strength of these processes were not measured, only rough estimates of the contribution of these sources could be provided (Sörgel et al., 2011c). Secondly, NO 2 and HONO exhibit different temporal variability, due to different chemical time scales. The NO 2 lifetime with respect to photolysis is about a factor of three lower than that of HONO. On the other hand, the formation of NO 2 by oxidation of NO (by O 3 or HO 2 ) is faster than the formation of HONO from NO 2 . Weaker correlations of HONO and NO 2 (both daytime and nighttime) in distance to emission sources (cities) have been observed by Harrison et al. (1996). A very recent PhD thesis by Pöhler (2010) employed a DOAS with different light paths using tomography to infer two-dimensional trace gas distributions of HONO and NO 2 . HONO displayed a much lower spatial variability than NO 2 , presumably due to the slow heterogeneous formation (Pöhler, 2010). Thirdly, if HONO mixing ratios are governed by surface water absorption (and thus RH), the HONO signal but not the NO 2 signal would be modulated by this effect. A detailed model approach which solves boundary layer dynamics, chemistry and effects of turbulence on chemistry as well as surface modifications (wetting) is required to solve this issue. Synthesis 21 Figure 7: a) Unknown HONO daytime source (P unknown ) in ppt h -1 versus j(NO 2 ). b) P unknown normalized by NO 2 mixing ratios yielding a conversion frequency (% h -1 ). Figure a) contains only data points (N = 753) which could be normalized to NO 2 . Points where ΔHONO/Δt was larger than the relative error of the LOPAP (± 12 %) are marked as filled red points. Blue dashed lines are linear fits to the data yielding a) r² = 0. 16 and b) r² = 0.38. The grey dashed line in Fig. 7b presents an upper limit based on the mean of the five lowest points at (jNO 2 ) min and five highest points at (jNO 2 ) max . Taken from Sörgel et al. (2011c). Two reactions forming HONO via light-induced NO 2 conversion were investigated in detail, as most parameters to calculate the HONO formation rate by these reactions were measured, i.e. the reduction of NO 2 on irradiated soot (Monge et al., 2010) and the reaction of electronically excited NO 2 with water vapor (Crowley and Carl, 1997; Li et al., 2008). The latter reaction, which forms HONO and OH in equal amounts, raised special attention and controversial discussion since the publication of Li et al. (2008). These authors found a rate constant for this reaction which was an order of magnitude higher than that originally measured by Crowley and Carl (1997) and confirmed by Carr et al. (2009). This higher value would have a substantial impact on the oxidation potential (Wennberg and Dabdub, 2008; Sarwar et al., 2009; Ensberg et al., 2010). During the DOMINO campaign the reaction of NO 2 * with water vapor was found to contribute less than 10 % to HONO formation even by taking the value of Li et al. (2008) as an upper limit (Sörgel et al., 2011c). A very recent study (Amedro et al., 2011) confirmed the lower value for the reaction rate constant measured by Crowley and Carl (1997) and Carr et al. (2009). Thus, this reaction contributed less than 1 % to P unknown . The same negligible contribution (< 1 %) was calculated for the reaction of NO 2 on irradiated soot for the conditions during DOMINO. Thus, for rural conditions (low NO x and black carbon) both reactions do not substantially contribute to HONO daytime formation. Synthesis 22 4.3 HONO vertical exchange in a forest environment It is highly probable that HONO is formed heterogeneously mainly at ground surfaces both day and night. A forest provides a large surface area and, separated by the canopy, different environments with respect to light and humidity. During IOP I, HONO was measured just above the canopy (24.5 m) and close to the forest floor (0.5 m). The temporal evolution of the mixing ratios at the different heights and the corresponding mixing ratio differences have been analyzed with special emphasis on turbulent mixing (Sörgel et al., 2011a). For this detailed analysis the so called Golden Days of IOP I (20-25 th of September 2007) where chosen. This was a warm and dry period between two rain events. The most astonishing but also clear result was that mixing ratio differences were around zero in the late morning to early afternoon (Fig. 8). Due to a longer lifetime of HONO (by a factor 10 to 30) below canopy because of the shading by the canopy, huge concentration differences had been expected. This discrepancy could be explained by intense vertical mixing as indicated by the coupling regimes. The coupling regimes denote which part of the canopy is coupled to the air layer above, and thus indicates which part of the canopy takes part in the exchange of energy and matter (Thomas and Foken, 2007). During the period when concentration differences were close to zero the canopy was either fully coupled or coupled by sweeps with only intermittent decoupling of the subcanopy. Already in the afternoon (starting at 13:00 CET) the subcanopy becomes decoupled from the air layer above and mixing ratio differences increase. During this period mixing ratio differences were always negative (i.e. below canopy values higher than above) and exhibited low variability (Fig. 8). Around sunset the whole forest became decoupled from the air layer above and during night, wave motion dominated. Thus, vertical exchange was limited and different sources and sinks (above and below canopy) became obvious. After sunset, differences became even more negative (up to – 170 ppt), mainly caused by increasing values below canopy. In the absence of light (photolysis) this was attributed to local HONO formation below canopy (Sörgel et al. 2011a). Around 21:00 CET the sign in mixing ratio differences changed due to increasing values above canopy. For some cases this could be attributed to advection of HONO-enriched air above canopy, which only partly penetrated into the canopy leading to mixing ratio differences up to ~240 ppt. The fact that advection became visible only after sunset can be related to the increasing influencing area due to the increase in HONO lifetime (from about 10 min at noon to >> 1 h at night; cf. Fig. 3). The nearest (~ 30 km) relevant sources are the cities of Kulmbach and Bayreuth and the motorway A9 (9 km). Thus, taking a wind speed of Synthesis 23 5 m s -1 , transport from these sources would require a time of 100 min (> 2 h) and 30 min, respectively. Sörgel et al. (2011a) speculate if the constantly higher HONO values above canopy during the late night (apart from advection events) could be attributed to the wetting of the canopy due to water adsorption, caused by radiative cooling of the canopy top. Coadsorbing water replaces HONO at the surfaces, according to the mechanism provided by Trick (2004). The only clear influence of surface water was the scavenging of HONO at relative humidities > 95 %, which structured the HONO time series due to rain events associated with the occurrence of synoptic systems. Therefore, the interplay of HONO and RH was studied further with a tool for time series analysis (Sörgel et al., in preparation). Fig. 8. Box-and-whisker plot for coupling regimes (red open bars) and HONO mixing ratio differences (grey filled bars) for the five-day dry period 20–25 September 2007 at the “Waldstein-Weidenbrunnen” research site. Coupling regimes (right hand side) are: Wa (Wave motion ∼ no turbulent exchange), Dc (decoupled canopy ∼ whole canopy decoupled from the air layer above), Ds (decoupled subcanopy ∼ only subcanopy decoupled), Cs (coupled by sweeps ∼ canopy and subcanopy coupled by sweep motion) and C (fully coupled canopy). The upper panel shows the specific humidity difference between 21 m and the forest floor for comparison. The upper end of the boxes represents the 75th percentile, the lower end the 25th percentile and the line within the boxes the median. Whiskers denote the 10th (lower whisker) and 90th (upper whisker) percentiles. Outliers are marked as points (HONO difference) or squares (coupling regimes). If only whiskers appear, there are no other values between the values marked by the whiskers. For the boxes at 10:00 and 14:00 CET the median falls in line with the lower end of the boxes (Ds). Taken from Sörgel et al., 2011a. Synthesis 24 4.4 Influence of RH on HONO mixing ratios Fig. 9. Simultaneous time series of HONO (right hand scales) at 0.5 m (lower graph, circles and lines) and 24.5m (upper graph, circles and lines), overlaid with a contour plot of the vertical profile of measured RH (left hand scale and color coded 50–92 %) for 23 September 2007 at the “Waldstein-Weidenbrunnen” research site. Missing values in the HONO measurements are due to zero air measurements. Sunrise and sunset (inferred from j(NO 2 ) and global radiation measurements) are marked as vertical (orange) lines. The upper panel shows the mixing ratio differences between 24.5 m and 0.5 m (1HONO) and the coupling regimes in the forest. Taken from Sörgel et al. (2011a). In all time series (IOP I, IOP II and DOMINO) HONO seemed to be influenced by RH (covariation, signal dampening, increase or decrease correlated). An example is given in Fig. 9, where HONO measurements at two different heights were overlaid with a contour plot of the relative humidity profile on a clear and dry day (IOP I). The canopy height was 23 m. During night, HONO features at both heights seemed to be correlated to features in RH which extend throughout the canopy. In the morning, RH and HONO decrease due to increasing radiation. HONO mixing ratios decrease due to photolysis and RH due to surface heating. The peak in HONO and RH around noon was associated with passing clouds and a change in wind direction. The increase in HONO and RH in the late afternoon was caused by the decoupling of the forest and subsequent accumulation of HONO and water vapor emitted at the ground. The sharp increase of both quantities at 21:00 CET was caused by an air mass change. Thus, Synthesis 25 for only one day several different correlations could be identified for different reasons (Sörgel et al., 2011a). This result stressed the importance to use tools for time series analysis in order to investigate causality between HONO and RH. Starting with “classical statistics” and taking all values from all campaigns, the linear correlation of HONO and RH was low (r=0.01). A first statistical analysis revealed that HONO values were log-normally distributed, whereas RH values were normally distributed or showed a bimodal distribution. Thus, for a linear correlation after Pearson the logarithmic HONO values have to be correlated with the RH values. This indeed improves the correlation, but mainly due to daytime data. Further improvement in the coefficient of determination was achieved by excluding HONO values measured in marine air masses during DOMINO which might be influenced by equilibrium with the sea surface (Wojtal et al., 2010). Nevertheless, taking only nighttime data, the coefficient of determination is still low (r² ~ 0.13). Part of the influence on the relation of HONO and RH might arise from variations of the HONO precursor NO 2 , but correlations of HONO and NO 2 were weak as well, especially for IOP I (r² = 0.14 at 24 m and r² = 0.05 at 0.5 m). The very weak correlation of HONO with its precursor NO 2 was the main motivation to think about the influence of RH. Although correlations of HONO with NO 2 were higher for IOP II and particularly good for DOMINO (r² = 0.44), this reflects more a tendency (higher NO 2 = higher HONO) than a strong correlation. This might be attributed to the rather slow formation of HONO from NO 2 (max. 2 % h -1 ). This means that HONO mixing ratios build up slowly are thus more evenly distributed, whereas NO 2 values might be quite variable as indicated by the results of Pöhler (2010) and Harrison et al. (1996). Therefore, HONO values were not normalized to NO 2 to avoid disturbances of the HONO to RH correlation due to variations in NO 2 . Usually, normalization to NO 2 is done to account for changes in boundary layer height and precursor concentration. Some shortcomings of this scaling approach have already been discussed by Su et al. (2008 a). By applying Singular System Analysis (SSA), correlations on different time scales can be found and may possibly allow for a separation of HONO and NO 2 and HONO and RH correlations. SSA (e.g. Elsner and Tsonis, 1996) was chosen for this analysis as it provides the opportunity to reconstruct the time series by using signal contributions associated to certain time scales. This is necessary to remove the signal contributions of the diurnal cycle of HONO and RH and the long term trends in order to identify correlations on shorter time scales which might be a hint at the interaction due to fast physical processes (adsorption/desorption). Synthesis 26 The SSA analysis of the HONO and RH time series revealed that the main signal contributions were the long term trends and the diurnal cycle (about 98 % for RH and 50-80 % for HONO). Therefore, it was rather challenging to extract correlations from the remaining signal as this contained both signal and noise. However, as can be seen in Fig. 10, the diurnal cycle and the long term trends are effectively removed by this technique. Only during the rainy periods (RH > 95 %) with almost constant values (RH ~ 98 - 100 %, HONO ~ 15 – 40 ppt) this method induced a non-existent diurnal cycle (Fig. 10, 18.09 - 19.09). Filtered time series (without long term trends and diurnal cycle) of HONO were weak but positively correlated to the filtered RH signals pointing to at least some influence of RH on HONO apart from diurnal cycle and long term trends. 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Appendix C: Sörgel, M., Trebs, I., Serafimovich, A., Moravek, A., Held, A., and Zetzsch, C.: Simultaneous HONO measurements in and above a forest canopy: influence of turbulent exchange on mixing ratio differences, Atmos. Chem. Phys., 11, 841–855,doi:10.5194/acp-11-841-2011, 2011. Appendix D: Sörgel, M., Held, A., Zetzsch, C.: Singular System Analysis of forest observations of HONO and humidity, to be submitted to Atmos. Envion., 2012. Appendix A 39 Appendix A Individual contributions to the publications This cumulative thesis consists of several manuscripts which originate from a close collaboration with other researchers. In this section the individual contributions are specified. Appendix B Sörgel, M., Regelin, E., Bozem, H., Diesch, J.-M., Drewnick, F., Fischer, H., Harder, H., Held, A., Hosaynali-Beygi, Z., Martinez, M., and Zetzsch, C.: Quantification of the unknown HONO daytime source and its relation to NO 2 , Atmos. Chem. Phys., 11, 10433-10447, doi:10.5194/acp-11-10433-2011, 2011. - I myself had the idea, did the calculations, made the graphs and wrote the manuscript. I also performed the HONO measurements. Furthermore, I responded to the referee comments and wrote the revised manuscript. - Eric Regelin performed the OH measurements together with Hartwig Harder and Monica Martinez. He also calculated the photolysis frequency of ozone with a radiation transfer model (TUV). Furthermore, Monica Martinez proposed corrections to the manuscript and provided helpful suggestions. - Heiko Bozem did the j(NO 2 ) measurements. - Jovana Diesch and Frank Drewnick measured the meteorological parameters (RH, wind speed, wind direction, atmospheric pressure), black carbon, ozone and water vapor mixing ratios. Additionally, Frank Drewnick helped to improve the manuscript with suggestions and questions as well as language corrections. - Andreas Held substantially helped to improve the structure and language of the manuscript. He participated consistently with discussions. He also helped to improve the replies to the referees as well as the revised manuscript. - Zeinab Hosaynali-Beygi performed the NO x measurements and wrote the experimental part regarding the NO x measurements. Additionally, Horst Fischer provided helpful comments to the manuscript. Appendix A 40 - Cornelius Zetzsch supported my participation in the DOMINO campaign. He also contributed to the preparation of the manuscript with discussions and suggestions and also helped to improve it by language corrections. - Two anonymous referees provided critical questions and suggestions to further improve the manuscript. Appendix C Sörgel, M., Trebs, I., Serafimovich, A., Moravek, A., Held, A., and Zetzsch, C.: Simultaneous HONO measurements in and above a forest canopy: influence of turbulent exchange on mixing ratio differences, Atmos. Chem. Phys., 11, 841–855,doi:10.5194/acp-11-841-2011, 2011. - I myself had the idea, did the calculations, made the graphs and wrote the manuscript. I also performed the HONO measurements at the forest floor. Furthermore, I replied to the referee comments and wrote the revised manuscript. Several discussions and suggestions led to the ideas of the manuscript. The input given during discussion by people who are not coauthors of this paper but are acknowledged in the manuscript (Ralph Dlugi, Thomas Foken, Jörg Kleffmann and Franz Meixner) helped developing the ideas. Also Eva Falge gave important input to several aspects of the manuscript. - Ivonne Trebs performed the HONO measurements above canopy and furthermore contributed to the manuscript with discussions, suggestions and corrections. She also provided the final data of the j(NO 2 ) and SMPS measurements which were performed by Jörg Sintermann and Daniel Plake. - Andrei Serafimovich performed the measurements of the eddy-covariance profile and the subsequent calculations of the coupling regimes. - Alexander Moravek performed the NO x and O 3 measurements. - Andreas Held substantially helped to improve the structure and language of the manuscript. He participated consistently with discussions. He also helped to improve the comments to the referees as well as the revised manuscript. - Cornelius Zetzsch proposed the original project to the DFG, supported the preparation of the manuscript with discussions and suggestions. He also helped to improve the manuscript by language corrections. Appendix A 41 - Data for temperature, relative humidity, precipitation and visibility are courtesy of the Department of Micrometeorology of the University of Bayreuth. SODAR data were provided by Stephanie Schier. - Two anonymous referees provided critical questions and suggestions to further improve the manuscript. Appendix D Sörgel, M., Held, A., Zetzsch, C.: Singular System Analysis of forest observations of HONO and humidity, to be submitted to Atmos. Envion., 2012. - I myself developed the idea, did the calculations, made the graphs and wrote the manuscript. I also performed the HONO measurements at the forest floor in IOP I and in the other campaigns. The idea to use Singular System Analysis for this kind of investigation was provided by Michael Hauhs. - Andreas Held substantially helped to improve the structure and language of the manuscript. He participated consistently with discussions. - Cornelius Zetzsch proposed the original project to the DFG, supported the preparation of the manuscript with discussions and suggestions. He also helped to improve the manuscript by language corrections. - Data for relative humidity (RH) during IOP I and IOP II are courtesy of the department of micrometeorology of the University of Bayreuth. RH data for DOMINO are courtesy of Jovana Diesch and Frank Drewnick from the Max Planck Institute for Chemistry, Mainz. During IOP I, HONO data above the canopy (IOP I 24.5 m) were provided by Ivonne Trebs, Max Planck Institute for Chemistry, Mainz. Appendix B 42 Atmos. Chem. Phys., 11, 10433–10447, 2011 Appendix B Quantification of the unknown HONO daytime source and its relation to NO 2 M. Sörgel 1,2 , E. Regelin 3 , H. Bozem 3* , J.-M. Diesch 4 , F. Drewnick 4 , H. Fischer 3 , H. Harder 3 , A. Held 2 , Z. Hosaynali-Beygi 3 , M. Martinez 3 and C. Zetzsch 1,5 1 University of Bayreuth, Atmospheric Chemistry Research Laboratory, Bayreuth, Germany 2 University of Bayreuth, Junior Professorship in Atmospheric Chemistry, Bayreuth, Germany 3 Max Planck Institute for Chemistry, Atmospheric Chemistry Department, P. O. Box 3060, 55020 Mainz, Germany 4 Max Planck Institute for Chemistry, Particle Chemistry Department, P. O. Box 3060, 55020 Mainz, Germany 5 Fraunhofer Institute for Toxicology and Experimental Medicine, Hannover, Germany * now at University Mainz, Institute for Atmospheric Physics, Mainz, Germany Received: 1 April 2011 – Published in Atmos. Chem. Phys. Discuss.: 18 May 2011 Revised: 29 September 2011 – Accepted: 8 October 2011 – Published: 20 October 2011 Abstract During the DOMINO (Diel Oxidant Mechanism In relation to Nitrogen Oxides) campaign in southwest Spain we measured simultaneously all quantities necessary to calculate a photostationary state for HONO in the gas phase. These quantities comprise the concentrations of OH, NO, and HONO and the photolysis frequency of NO 2 , j(NO 2 ) as a proxy for j(HONO). This allowed us to calculate values of the unknown HONO daytime source. This unknown HONO source, normalized by NO 2 mixing ratios and expressed as a conversion frequency (% h -1 ), showed a clear dependence on j(NO 2 ) with values up to Appendix B 49 Atmos. Chem. Phys., 11, 10433–10447, 2011 MoLa measured ozone by UV absorption with the “Airpointer” (Recordum, Mödling, Austria), water vapour mixing ratios by infrared absorption (LICOR 840, Li-COR, Lincol, USA) and black carbon with a Multi Angle Absorption Photometer (MAAP, Model 5012, Thermo Fischer Scientific, Whatman, USA). 3 Results and discussion 3.1 Meteorological and chemical conditions Figure 1 gives an overview of meteorological and chemical measurements during the experiment in November/December 2008. In the beginning of the campaign there was a fair weather period with moderate (about 3 m s -1 ) north-easterly winds (from inland Seville region). On November 24, the wind direction changed to northwest (along the coast from Huelva). From the 28 th to 30 th November, clean marine air with some plumes arrived at the site from the west. This was also the only period with rainfall, and HONO values were often around the detection limit (2 ppt). Ozone mixing ratios were about 30 ppb and showed a diurnal variation except for the clean air period with higher values (40 ppb) and no diurnal variation. A more detailed analysis of the ozone behaviour and the different wind sectors has been given by Diesch et al. (2011). Appendix B 50 Atmos. Chem. Phys., 11, 10433–10447, 2011 Figure 1: Overview of meteorological (RH, wind speed and wind direction) and chemical quantities (O 3 , NO, NO 2 , HONO, HONO PSS (calculated), HONO/NO x and HONO/NO 2 ratios and j(HONO)). 3.2 Photostationary state (PSS) 3.2.1 Calculating the photostationary state/gas phase Regarding only the well-established gas phase formation (R7) and gas phase sink processes ((R5) and (R6)) one can calculate the photostationary state (PSS) mixing ratio of HONO (Cox, 1974; Kleffmann et al., 2005), [ ] )(][ ]][[ 6 7 HONOjOHk OHNOk HONO PSS + = (1) [HONO PSS ] is the equilibrium concentration, [NO] and [OH] are the measured NO and OH concentrations, and j(HONO) is the photolysis frequency of HONO. Rate constants for the termolecular reaction (R7) were calculated at atmospheric pressure from the fall-off curves (high and low pressure limit rate constants) according to the formulas given by the respective references (Atkinson et al., 2004; Sander et al., 2006). Values of k 7 differed by 24 % Appendix B 51 Atmos. Chem. Phys., 11, 10433–10447, 2011 (constantly over the temperature and pressure range of our study): from IUPAC (Atkinson et al., 2004) k 7,(298 K) = 9.8 x 10 -12 cm 3 molecules -1 s -1 and from JPL (Sander et al., 2006) k 7,(298 K) = 7.4 x 10 -12 cm 3 molecules -1 s -1 . The calculated JPL value is consistent with the value (k 7,(~298 K) = (7.4 ± 1.3 ) x 10 -12 cm 3 molecules -1 s -1 ) measured directly at atmospheric pressure by Bohn and Zetzsch (1997). We therefore prefer this value and use it for our calculations of the PSS. For the bimolecular reaction of HONO and OH (R6), a rate constant of k 6,298 K = 6.0 x 10 -12 cm 3 molecules -1 s -1 was taken from Atkinson et al. (2004). Uncertainties in the PSS mainly originate from OH measurements with an accuracy of ± 18 %. This has some influence on HONO formation via (R7) but not much influence on the loss term, since HONO loss via (R7) was mostly less than 5 % of the total loss ((R5) + (R6)) during the whole campaign. As OH measurements may possibly suffer from interferences, the [HONO PSS ] values are rather an upper limit. As a consequence, the unknown HONO source discussed in Sect. 3.3 is rather a lower limit. There is also some uncertainty in the j(HONO) values since the portions of the upwelling part of the radiation measured at the site were about 0.3-0.5 of the downwelling (direct + diffuse). These high albedo values were presumably caused by the white container roofs and the aluminium scaffold below the sensor. As the minimum HONO lifetime (inverse photolysis frequency) is about 15 min around noon, our measurements at the 10 m scaffold do not reflect the local situation but an integration over a “footprint area” (Schmid, 2002; Vesala et al., 2008). Therefore, we chose an albedo value for UV radiation of the surrounding pine forest of 0.05 (Cancillo et al., 2005) which is more representative. Appendix B 52 Atmos. Chem. Phys., 11, 10433–10447, 2011 Figure 2: Daytime cycles of a) measured HONO mixing ratios, HONO meas b) calculated HONO mixing according to Eq. (1), HONO PSS c) NO 2 and d) NO mixing ratios as well as e) HONO/NO x ratios with the value of 0.8 % for direct emissions (Kurtenbach et al., 2001) marked as black line and black carbon concentration f). The boxes and whiskers represent a one hour time interval (centred in the middle) of five minute data (22-72 data points) of 7 cloud free days (21 ,22, 23, 25, 26, 27 th November and 2 nd December). The upper ends of the boxes represent the 75 th percentile, the lower bounds the 25 th percentile and the line within the boxes the median. The upper whisker marks the last point within the 90 th percentile and the lower whisker that of the 10 th percentile. Data points outside the 10 th and 90 th percentile are marked individually as dots. Appendix B 53 Atmos. Chem. Phys., 11, 10433–10447, 2011 Figure 2 summarizes the diurnal courses of HONO and NO x for 7 cloud free days. On the 27 th around noon, fair weather clouds were passing. These data points were rejected for further analysis to exclude effects from fluctuations in j(HONO). On 2 nd of December, data was taken from a second LOPAP at 1 m height as there were no data available from the 10 m instrument. Both instruments have been demonstrated to agree within 12 % under dry field conditions in side-by-side measurements (Sörgel et al. 2011). Assuming efficient vertical mixing during the day, HONO mixing ratios at 1 m and 10 m height can be expected to be similar (Sörgel et al., 2011). The portion of HONO formed by known reactions in the gas phase ([HONO PSS ], Fig. 2 b) is not negligible. The median contribution is 20 % (25 percentile is 13 %) of the measured HONO mixing ratios. On the other hand, the gas phase formation can explain only part of the measured HONO, as 75 % of the [HONO PSS ] values contribute less than 30 % to the measured values. HONO meas , HONO PSS , NO and NO 2 have a similar diurnal cycle with the most pronounced feature being the maximum values around 9:00 UTC. This could be explained by local emissions which were trapped in the stable boundary layer before the breakup of the inversion in the morning. In the afternoon (15:00-16:00), this peak occurs less pronouncedly in NO and NO 2 but very clearly in the PSS values, as OH values are about twice (~ 3x10 6 molecules cm -3 ) those at 9:00. From Figs. 2 b and d one can infer that [HONO PSS ] values are correlated to NO mixing ratios (r²=0.78). Correlations to other input parameters of the PSS are low ([OH] r² = 0.006; j(HONO) r² = 0.01). Therefore, NO availability seems to be a driving force for HONO gas phase chemistry. Measured HONO mixing ratios (Fig. 2a) have a coefficient of determination r² = 0.49 with [NO 2 ], and r²= 0.36 with [NO]. The relation of the HONO formation rate (which is more appropriate than HONO mixing ratios) and NO 2 is discussed in detail in chapter (3.3). HONO/NO x ratios reach their daytime maximum in the early afternoon with median values around 4 % (Fig. 2 e), implying efficient NO x conversion. On the other hand, the maximum can also be attributed to sources independent from ambient NO x values such as soil emissions (Su et al., 2011), and HNO 3 photolysis at surfaces (Zhou et al., 2011), which are not affected by the declining NO x values in the early afternoon. Appendix B 54 Atmos. Chem. Phys., 11, 10433–10447, 2011 3.2.2 Including the parameterized heterogeneous HONO formation into PSS calculations To sum up known HONO formation pathways, the heterogeneous formation ((R1)/(R2)) which was measured during nighttime may be included as an additional source in the PSS (e.g. Alicke et al., 2002; Alicke et al., 2003) with the assumption that ((R1)/(R2)) continue at daytime in the same manner as at night. This assumption may not be true because even at night HONO formation (release) is not proceeding at the same rate all night. Studies about HONO fluxes (Harrison and Kitto, 1994; Harrison et al., 1996; Stutz et al., 2002; Stutz et al., 2004) explained that measured HONO formation is a net process (pseudo steady state) of release and deposition (see also discussion in Vogel et al. (2003)). A recent study by Wong et al. (2011) provides detailed information about HONO formation and deposition in the Nocturnal Boundary Layer (NBL) by combining vertical gradient measurements with 1-D model calculations. According to their results the ground surface accounts for most (~70%) of the HONO formation by NO 2 conversion but also for most of the loss (~70%). This confirms previous results from ground based field measurements (Harrison and Kitto, 1994; Stutz et al., 2002; Veitel, 2002; Kleffmann et al., 2003; Zhang et al., 2009; Sörgel et al., 2011), aircraft profiles (Zhang et al., 2009) and modelling (Vogel et al., 2003) that the ground surface is a major source of HONO. Hence, turbulent exchange has a significant impact on near surface HONO mixing ratios as already proposed by Febo et al. (1996). These authors found a good correlation of HONO with radon, which is exclusively emitted from the ground. Furthermore, profiles from recent aircraft measurements were closely related to atmospheric stability with higher HONO values close to the ground and steeper gradients during stable conditions (Zhang et al., 2009). Therefore, mixing ratios are also expected to be controlled by the mixed volume which determines the surface to volume ratio (S/V). The conventional way to account for changes in S/V is the scaling of HONO or HONO production (P HONO ) by NO 2 or NO x (e.g. Alicke et al., 2002; Alicke et al., 2003). It is assumed that NO x is also emitted close to the ground, and therefore is also sensitive to S/V and NO 2 is the precursor of HONO. As local sources/sinks of the compounds used for scaling (e.g. NO x ) may disturb the HONO/NO x ratio, Su et al. (2008a) proposed a combined scaling using also black carbon (BC) and carbon monoxide (CO). To our knowledge, only two recent studies (Yu et al., 2009; Sörgel et al., 2011) tried to address S/V (ground and aerosol) directly by using inversion layer heights from SODAR Appendix B 55 Atmos. Chem. Phys., 11, 10433–10447, 2011 measurements to estimate mixed volumes. However, at night a stable boundary layer is formed where only intermittent turbulence provides some mixing (Stull, 1988). Therefore, a mixed volume cannot easily be defined. Apart from that, NO 2 conversion frequencies measured in different environments around the world are all within a quite narrow range from 0.4 to 1.8 % h -1 as summarized by Su et al. (2008a) and Sörgel et al. (2011). Conversion frequencies (F HONO, night ) of 0.9-2 % h -1 for individual nights and a mean value of 1.5 ± 0.6 % h -1 were derived in this study using the approach of Alicke et al., (2002). In our study, nighttime HONO formation occurs presumably by (R1) and (R2). Formation through (R3), (R4) and (R7), all involving NO, is not considered to be important since HONO typically increased from sunset (17:30 UTC) to midnight, when NO mixing ratios were mostly (93 %) below the detection limit (LOD) of 6 ppt. Only 87 of 1232 five-minute mean values were above the LOD with median mixing ratios of 8 ppt, respectively. Therefore, a linear regression of the HONO/NO x ratio and HONO/NO 2 ratio for all night time data yields a slope of 1.0 and an intercept of 0.02 % (r²=0.9986). Thus, both ratios can be regarded as equivalent during nighttime. There are no clear indications about the contribution of direct emissions. The closest emissions sources were the industrial area of Huelva (shortest distance ~15 km) and the city of Huelva (city centre about 20 km). Thus, transport times are in the range from one to two hours. Applying a conversion frequency for NO 2 to HONO of about 1 % h -1 , which is within the range of published values (see above), yields a 1-2 % increase in HONO/NOx during the transport. Thus, HONO mixing ratios reaching the site are already two to threefold those originally emitted (HONO/NOx ~0.8 %, Kurtenbach et al., (2001)). Using the wind sector classification for the DOMINO site of Diesch et al. (2011) we found indeed lower HONO/NOx values at night for air masses passing Huelva than for other air masses from the continent. If this can be attributed to direct emissions is unclear. The transport occurs along the coast and therefore also mixing with HONO depleted marine air can cause lower HONO/NOx. HONO/NOx values for Huelva are indeed within the range of those for the “clean” marine sector. Therefore, we assume that (R1)/(R2) is the dominant nightime HONO formation pathway at the DOMINO site. Generally, a stable boundary layer is formed at nighttime in which turbulence is suppressed, whereas during daytime a mixed layer develops which is much more turbulent (e.g. Stull, 1988). This has two opposing effects on (R1) and (R2) (especially if the ground surface is the dominant source). Appendix B 56 Atmos. Chem. Phys., 11, 10433–10447, 2011 1) During daytime turbulence is enhanced which means that NO 2 is efficiently transported to the reactive surface. 2) The surface to volume ratio (S/V) is lower during daytime, as the mixed volume increases (mixed layer), thus less reactive surface area per volume is available. If no deposition or advection occurs, HONO/NO x will rise continuously from sunset to sunrise, as photolysis is absent. We found decreasing HONO/NO x in the late night until sunrise which may point to the dominance of loss processes of HONO, e.g. deposition. Therefore, it is questionable if (R1) and (R2), i.e. heterogeneous formation, can simply be transferred to daytime conditions. As will be shown in Sect. 3.3 (Figs. 3 and 4), including this dark heterogeneous source as a daytime source in Eq. (3) to calculate the magnitude of the unknown daytime source P unknown yields mainly negative values in the early morning. This points to a missing sink like deposition (or a smaller source or both). Therefore, we did not consider this heterogeneous source for the PSS calculations. 3.3 Missing daytime source As shown in Sect. 3.2 (Fig. 2) measured HONO values (HONO meas ) almost always exceed the [HONO] PSS values. Thus, an additional (unknown) HONO daytime source exists. Equation (2), which is similar to that of Su et al. (2008b), sums up the processes influencing HONO mixing ratios. hvdepOHHONOphotunknownhetemisOHNO TTLLLPPPP kssources dt dHONO ++++−+++= =−= ++ )()( sin (2) The source/production (P x ) terms consist of the gas phase formation (P NO+OH , (R7)), the dark heterogeneous formation (P het , via (R1)/(R2)) and direct emissions (P emis ). P unknown is the unknown HONO daytime source. The sink/loss processes (L y ) are photolysis (L phot , (R5)), reaction of HONO with OH (L HONO+OH , (R6)), and dry deposition (L dep ). Note that the terms for vertical (T v ) and horizontal advection (T h ) can mimic source or sink terms depending on the HONO mixing ratios of the advected air relative to that of the measurement site (and Appendix B 57 Atmos. Chem. Phys., 11, 10433–10447, 2011 height). If HONO has a ground source (or near surface aerosol source), T v mimics a sink term, as vertical mixing dilutes HONO formed near the ground (see also discussion 3.2.2). The magnitude of T v (without the contribution of the rising boundary layer in the morning) can be estimated by using a parameterization for dilution by background air provided by Dillon et al. (2002), i.e. T v = k (dilution) ([HONO]-[HONO] background ). Assuming a k (dilution) of 0.23 h -1 (Dillon et al., 2002), a [HONO] background value of about 10 ppt (Zhang et al., 2009) and taking mean noontime [HONO] values of 35 ppt we can derive that T v is about 4 ppt h -1 . This value is about the same magnitude as L dep as already suggested by Su et al. (2008b). L dep can be parameterized by multiplying the measured HONO concentration with the dry deposition velocity and then scaling by the mixing height, in order to scale the loss at the ground to its contribution to total HONO loss in the mixed volume. Taking a deposition velocity of 2 cm s -1 (Harrison et al., 1996, Su et al., 2008b) and a mixing height of 1000 m, L dep is in the order of a few ppt h -1 in our study which is indeed small (<3 % of L phot 09:0015:30 UTC for 7 clear days N=312) compared to L phot . As is discussed in more detail later, the relative contribution of L dep might be higher in the morning and evening hours, as L phot is smaller and a stable boundary layer is formed (mixed height << 1000 m, or stable conditions). Overall, T v and L dep are small loss terms (compared to L phot ). If their contributions are larger than assumed (especially in the morning and evening), P unknown is underestimated during these periods. P emis cannot easily be determined, because its contribution varies with the source strength, the HONO lifetime, the horizontal wind speed and wind direction. Again, this contribution is assumed to be highest in the morning and in the evening (longer lifetimes = longer transport range). As there were no collocated emission sources, directly emitted HONO only contributed to the horizontal advection term (T h ). Measured HONO/NO x ratios were always higher than those reported for direct emissions (max. reported 0.8 %) (Pitts et al., 1984; Kirchstetter et al., 1996; Kurtenbach et al., 2001; Kleffmann et al., 2003). Thus, no pure direct emissions were measured. Therefore, the contribution of directly emitted HONO to the HONO budget is uncertain, but P emis can be assumed to be of minor importance around noon, as NO x values exhibit a minimum and show low variability. Furthermore, HONO lifetime is only about 15 min, so at typical wind speeds of about 3 m s -1 , emissions have to occur within 3 km to reach the site within their lifetime. Additionally, minimum values of HONO/NO x , which indicate fresh emissions, are independent of wind direction. Appendix B 58 Atmos. Chem. Phys., 11, 10433–10447, 2011 Simplifying Eq. (2), we can derive the unknown HONO daytime source, P unknown , from Eq.( 3). t HONO PPLLLP hetOHNOdepphotOHHONOunknown ∆ ∆ +−−++= ++ (3) P unknow is not equal to OH production from HONO as for net OH formation a simple balancing of gas phase sources and sinks without further assumptions is applicable (P OH = L phot -L HONO+OH -P NO+OH ). Mean diurnal contributions of the single terms and the values of P unknown are presented in Fig.3. P NO+OH , L phot , L HONO+OH were calculated from measured values as already described for the PSS (Sect. 3.2.1). P het was parameterized from the nighttime NO 2 conversion by ][)( 2, NOFtP nightHONOhet = (Alicke et al., 2002) using F HONO,night = 1.5 % h -1 (Sect. 3.2.2). The differential dHONO/dt was substituted by the difference ∆HONO/∆t, which is the mixing ratio difference from the centre of the interval (5 min) to the centre of the next interval (LOPAP has 5 min time resolution) and accounts for changes in mixing ratio levels. It became obvious that point to point changes in HONO (∆HONO/∆t) were mostly smaller than the relative error of the instrument (± 12 %), and so we could not account for these changes. Values above this threshold were mainly caused by sharp HONO peaks which were accompanied with peaks in NO and BC. These plumes passed the site mainly in the morning hours (see Figs. 2, 3 and 4) with maximum HONO values comparable to the nighttime maxima (Fig.1). This indicates that especially in the morning, the advective term T h does play a role and the arrival of plumes at the site mimics a source term (∆HONO/∆t > 0), whereas their fading (∆HONO/∆t < 0) mimics a sink (Figs. 3 and 4). Also, the contribution of ∆HONO/∆t to the HONO budget depends on the integration time of the HONO signal. Comparing 5, 15, 30 and 60 min values, the highest contribution is associated with the 5 min values and the lowest with the 30 min values (60 min values are possibly already influenced by the diurnal cycle). Besides less influence from advection, the lower contribution of ∆HONO/∆t to the source and sink terms during the PRIDE-PRD-2004 experiment (Su et al., 2008b) compared to our study could at least partly be caused by the lower time resolution for HONO measurements in that study. Appendix B 65 Atmos. Chem. Phys., 11, 10433–10447, 2011 at 20 ppb NO 2 . We scaled these values to 1 ppb NO 2 (observed NO 2 values). As already concluded by Stemmler et al. (2007) the contribution of the aerosol is negligible (~0.05 ppt h -1 ). The ground source would contribute about 35 ppt h -1 , i.e. one third of the missing source, applying a linear scaling with NO 2 . Regarding the soil emissions, there are no soil acidity and nitrate loading data available for the DOMINO campaign. Therefore, it is at best speculative to derive a HONO source based on the numbers given by Su et al. (2011) as the resulting HONO fluxes vary by orders of magnitude. But as HONO soil flux values in the lowest range (low nitrogen loading and rather high pH) can already produce source strength in the right order of magnitude for P unknown , this HONO source might be a substantial contribution during DOMINO. All calculations about source strength at the ground are very sensitive to vertical mixing. Thus, as already addressed by Zhou et al. (2011), vertical transport determines the discrepancy between the effective source strength relative to that calculated at the measurement height. We conclude that only modelling which takes vertical transport into account can yield reliable estimates of the ground source contribution to the missing HONO source. 3.5 Comparison of OH radical production from ozone and HONO photolysis OH production rates from ozone photolysis were calculated from ozone, H 2 O measurements and modelled jO( 1 D) values which were scaled by the ratio of measured and modelled j(NO 2 ). OH production from O( 1 D) was calculated according to Crowley and Carl (1997) using the rate constants for O( 1 D) quenching by O 2 , N 2 and O 3 and the reaction with H 2 O taken from the IUPAC recommendations (Atkinson et al., 2004 and updated values from the IUPAC homepage, http://www.iupac-kinetic.ch.cam.ac.uk/). These values are in good agreement (~ 3 % higher) with the same calculations using the recommendations from Sander et al. (2006). The net OH production by HONO was calculated by balancing source and sink terms of OH by HONO in the gas phase (for k values see Sect. 3.2): ]][[]][[])[( 67 OHHONOkOHNOkHONOHONOjP OH − − = (5) Appendix B 66 Atmos. Chem. Phys., 11, 10433–10447, 2011 Although HONO mixing ratios (mean: 30 ppt) are three orders of magnitude lower than O 3 mixing ratios (mean: 35 ppb) around noon and OH production rates by O( 1 D) exceed those of HONO photolysis by about 50 % around noon (11:00-13:00), the integrated daily OH production is about 20 % lower than that of HONO. Figure 7 shows the higher contribution of HONO photolysis to the OH formation in the morning and evening hours due to longer wavelengths (up to ~ 400 nm) associated with HONO photolysis. A special feature of our measurement site are the very high HONO values between 8:00 and 11:00, which can be attributed to advection (see Sect. 3.2 and 3.3). This leads to high P OH values from HONO photolysis during that period. Figure 7: Comparison for the seven clear days of the campaign of calculated primary OH production by HONO and ozone photolysis (means and standard deviations). 4 Conclusions The unknown HONO daytime source derived from our measurements was normalized by NO 2 mixing ratios to improve comparability of HONO source strengths in different environmental and laboratory conditions. For the nighttime formation of HONO, we can Appendix B 67 Atmos. Chem. Phys., 11, 10433–10447, 2011 exclude that NO plays an important role as NO was mostly below the detection limit of about 6 ppt. Inclusion of the parameterized nighttime HONO formation from NO 2 (1.5 % h -1 in this study) as an additional source into the calculations of the unknown HONO daytime source (P unknown ) yields mainly negative values in the early morning. This indicates the relevance of loss terms not taken into account (e.g. deposition) or overestimation of the dark heterogeneous formation in the morning and evening. Restricting the analysis only to cloud free days and the time around noon, when faster HONO photolysis leads to lifetimes around 15 min and other loss processes for HONO are small compared to loss by photolysis, establishment of a PSS can be assumed. The mean source strength of P unknown under these conditions was about 100 ppt h -1 and thus in the lower range of values reported in the literature. Nevertheless P unknown was the dominant HONO source during day. The normalized unknown HONO source (or NO 2 conversion frequency, if we assume that NO 2 is the precursor) varied from slightly negative values in the morning and evening to an upper limit correlated with j(NO 2 ). High median daytime NO 2 conversion frequencies of ~14 % h -1 were found around noon, in addition to the 1.5 % h -1 HONO formation rate observed during night. Our results indicate light-induced HONO formation, possibly via conversion of NO 2 as indicated by lab experiments. This source is about an order of magnitude stronger than HONO formation during nighttime. We compared the HONO net source to values calculated for light-induced NO 2 uptake on soot (Monge et al., 2010) and the reaction of electronically excited NO 2* with water vapour. The contribution of these reactions to HONO daytime values was mostly less than 10 % and cannot explain the HONO source strength derived in our study. Other processes like light-induced conversion of NO 2 on irradiated organic materials like humic acids (Stemmler et al., 2006), or soil emissions (Su et al., 2011) might be more important. Additional measurements including detailed speciation of organic aerosols and determination of humic acids on ground and canopy surfaces are needed to quantify their contribution. Furthermore, a detailed assessment of the contribution of the ground sources requires profound knowledge of boundary layer processes. 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Phys., 11, 841–855, 2011 the main tower (31 m walk-up tower) was used for (undisturbed) turbulence measurements. The forest floor exchange site was located about 30 m northwest of the main tower. Simultaneous measurements of HONO were conducted at a height of 24.5 m (just above canopy) on the main tower and close to the forest floor in 0.5 m at the forest floor exchange site from 13 to 25 Sep. 2007. HONO was measured by two LOPAP instruments (LOng Path Absorption Photometer, QUMA Elektronik & Analytik, Wuppertal, Germany). The LOPAP is based on a wet chemical technique, with fast sampling of HONO as nitrite in a stripping coil and subsequent detection as an azo dye using long path absorption in 2.4 m long Teflon AF tubing. A detailed description of the instrument has been given by Heland et al. (2001) and Kleffmann et al. (2002). The instruments were placed outside in the forest or directly on the tower in ventilated aluminum boxes without temperature control. The temperature of the stripping coils was kept constant at 20°C by thermostats to assure constant sampling conditions. From 27 Sep. to 3 Oct. both LOPAPs were compared side-by-side near the forest floor at a height of 1 m. The sampling inlets had a distance of about 50 cm and were directed northwards to sample perpendicularly to the westerly flow. No T-piece was used as inlet to avoid artificial HONO formation or adsorption on the inner walls of the tubing. Both instruments were supplied with the same reagents via T-pieces. Temperature and humidity profiles were measured at the main tower using Frankenbergertype psychrometers (Frankenberger, 1951). The relative humidity (RH) was calculated from the dry and wet bulb temperature of the psychrometers using the Magnus formula after Sonntag (1990) for the saturation vapour pressure and the Sprung formula for the actual vapour pressure (Foken, 2008). Psychrometers were mounted on the main tower at 0, 2, 5, 12, 21 and 32 m. Additionally, visibility was measured with a PWD11 (Vaisala,Vantaa, Finland) present weather detector mounted on the main tower. Aerosol number size distributions were measured on the main tower at 28 m height using a Scanning Mobility Particle Sizer (SMPS, Grimm, Ainring, Germany). Boundary layer heights were derived from SODAR (SOund Detection And Ranging, Metek Meteorologische Messtechnik, Elmshorn, Germany) measurements at a nearby clearing. Vertical profiles of nitrogen oxides (NO and NO 2 ) were measured on the main tower and on the forest floor exchange site by red-filtered detection of the chemiluminescence produced by the reaction of NO with O 3 (CLD 780 TR, ECO Physics, Duernten, Switzerland). NO 2 was photolytically converted to NO by exposure of the sample air to a solid-state blue-light converter (Meteorologie Consult, Königstein, Germany) and subsequently detected by the Appendix C 82 Atmos. Chem. Phys., 11, 841–855, 2011 chemiluminescence analyzer. Sample air was drawn through 55 m of non-transparent and heated PFA tubing from each inlet height. Inlet heights of the system were located at 0.05, 0.3, 1, 2, 5, 10, 16, and 24 m. The lower heights of up to 2 m were located at the forest floor exchange site, while the upper heights were mounted at the main tower, for details see Moravek (2008). To detect the coupling regimes between the subcanopy, canopy and layer above the canopy the eddy-covariance measurements were used. Six eddy-covariance systems consisting of sonic anemometers and fast response CO 2 and H 2 O gas analyzers were installed at the turbulence tower in 2.25, 5.5, 13, 18, 23 and 36 m. The wavelet transform was used to detect and extract ramp-like structures (coherent structures) from high frequency measurements of wind, sound temperature, CO 2 and H 2 O concentrations (Thomas and Foken, 2005). These structures are responsible for the turbulent coherent transport of the momentum and matter in forested ecosystems. Analysis of a sensible heat transport by coherent structures reveals the portions of the forest canopy coupled by coherent exchange with the air above the canopy (Thomas and Foken, 2007). The experimental setup and the analysis of the coupling regimes during the EGER intensive observation periods are described in more detail by Serafimovich et al. (2010). The HONO photolysis frequency (j(HONO)) was calculated from the NO 2 photolysis frequency measured by filter radiometers (Meteorologie Consult, Königstein, Germany) according to Kraus and Hofzumahaus (1998) and Trebs et al. (2009). The radiometers were mounted on top of the main tower at a height of 28 m, and at 2 m above the forest floor at the forest floor exchange site. For statistical computing the free statistics software “R” was used ( http://www.R-project.org. ). 3 Results and discussion 3.1 Comparison of the two LOPAP instruments The LOPAP instruments from the University of Bayreuth (UBAY) and the Max-PlanckInstitute for Chemistry (MPIC) were compared side-by-side at 1 m above the forest floor between 27 Sep. and 3 Oct. to evaluate the precision of the instruments. For this purpose the relative differences of the measured HONO mixing ratios were calculated by normalizing the difference of [HONO] UBAY minus [HONO] MPIC by the arithmetic mean of these mixing ratios. Appendix C 83 Atmos. Chem. Phys., 11, 841–855, 2011 The temporal evolution of these relative mixing ratio differences is shown in Fig. 1 together with the visibility as an indicator of foggy events. During rainy and foggy weather conditions indicated by the reduced visibility, we observed systematic deviations between the two LOPAP instruments. Large relative differences of the HONO signals during periods with low visibilities are related to fog events. Bröske et al. (2003) reported no measurable particle losses in the sampling glass coil for SOA (secondary organic aerosol) particles with diameters from 50-800 nm but Kleffmann et al. (2006) argued that large particles like fog droplets might be sampled by the coil. Thus, the sampling of fog droplets containing nitrite is a plausible explanation for deviations under wet conditions. However, this should affect both instruments by introducing more scatter and cannot explain the systematic deviation. Another potential reason could be that the surfaces of the inlets (first centimetre of the coils before contact with the sampling reagent) exhibited different wettabilites during these periods. Figure 1. Side-by-side measurements of the two LOPAP instruments from 27 Sep. (noon) to 3 Oct. 2007 (noon) at the “Waldstein-Weidenbrunnen” research site. Relative differences of the HONO signals (black dots) and visibility range (red squares, dashed lines, maximum range 2000 m). The insert shows the regression obtained during dry conditions (N = 247) from 29 Sep. (14:00 CET) to 2 Oct. (10:00 CET) using standard major axis (SMA) regression. The upper panel shows the mixing ratios measured by the two LOPAP instruments. Missing values are due to zero air measurements and calibration of the LOPAP instruments. Appendix C 84 Atmos. Chem. Phys., 11, 841–855, 2011 During the dry conditions (visibilities of more than 2000 m, which is the maximum detectable by the instrument) between 29 Sep. (14:00 CET) and 2 Oct. (10:00 CET) HONO levels ranged from 35 ppt to 170 ppt and the instruments agreed within 12 % (2σ), which is within the range of the estimated instrumental error under the given conditions (e.g., detection unit not air conditioned). Omitting the wet conditions before and after the dry period the relative errors correspond to a Gaussian distribution centred at zero. Thus, no systematic deviation of the instruments was found during dry conditions. The insert on Fig. 1 shows the correlation between the two instruments. Applying standard major axis regression analysis (Sokal and Rohlf, 1995; Legendre and Legendre, 1998), which is suitable for two random variables (e.g., Ayers, 2001) by reducing deviations perpendicularly to the regression line, yields an intercept of 2.3 ppt, which is close to the detection limit of the instruments (3σ-definition). The slope is 0.97 and the coefficient of determination r² = 0.98 for the dry weather period. Kleffmann (2006) used a T-piece and PFA tubing in front of the sampling units to compare two LOPAP instruments in order to avoid any influence from inhomogeneities in the sampled air. The linear correlation of these two LOPAPs was very good over a large mixing ratio range from about 200 ppt to 1.6 ppb, with a slope of 0.993 and an intercept of 1.4 ppt. However, since it is well known that any tubing in front of the sampling unit may cause artefacts due to wall reactions of NO 2 we avoided this approach. Furthermore, there was no dependency of the relative error on the friction velocity (u*) or the horizontal wind speed, indicating no significant influence from inhomogeneities in the sampled air. This is expected because the sampling units were only about 50 cm apart. At wind speeds as low as 0.5 m s -1 it took only 1 s to pass both sampling units, whereas the response time of both instruments is about 7 min. Thus, small scale inhomogeneities should contribute equally to both signals. We conclude that the LOPAP instruments can be used to reliably measure vertical HONO mixing ratio differences under dry conditions. 3.2 Factors controlling HONO mixing ratio levels 3.2.1 General observations in the time series HONO is effectively scavenged by precipitation due to its good water solubility with a Henry`s law constant of about 50 mol L -1 atm -1 (Sander, 1999). Precipitation was found to structure the time series ranging from 13 of Sep. to 3 Oct. on a time scale of about a week due Appendix C 85 Atmos. Chem. Phys., 11, 841–855, 2011 to synoptic weather conditions (low and high pressure systems). Although not precisely measurable (see section 3.1) due to large relative errors of both instruments during foggy conditions, HONO values were clearly lower at both heights during these wet periods. HONO/NO x ratios were below 2 % at both heights because (due to lower Henry`s law constants of NO (about 2x10 -3 mol L -1 atm -1 ) and NO 2 (1-4 x 10 -2 mol L -1 atm -1 ) (Sander, 1999)) NO x is not significantly influenced by precipitation scavenging or enhanced deposition on wet surfaces. During the entire IOP, three dry periods occurred between rain events. Dry periods were characterized by steadily increasing nighttime HONO mixing ratios up to 500 ppt, while HONO mixing ratios dropped during rain events to about 20 ppt. One of these dry periods (20 - 25 Sep.) is shown in Figs. 2 and 3. Below canopy measurements of HONO, NO x and HONO/NO x are presented together with rain fall measurements in Fig. 2. An overview graph for above canopy meteorological (wind direction, friction velocity, temperature, relative humidity and j(HONO)) and chemical (NO, NO 2 , HONO and ozone) measurements is given in Fig. 3. Figure 2. Time series of HONO at 0.5 m above the forest floor (red line), NO x (grey dashed line) and HONO/NO x ratio (black dotted line) from 18 (0:00 CET) to 26 (0:00 CET) Sep. 2007 at the “WaldsteinWeidenbrunnen” research site. This dry period was delimited by rain events marked with blue dots (half hourly values). Appendix C 86 Atmos. Chem. Phys., 11, 841–855, 2011 Figure 3. The upper panel shows the wind direction above canopy (32 m) and the friction velocity (u*) at the top of the canopy (21 m). In the middle panel the trace gases (NO, NO 2 , O 3 and HONO) measured above the canopy (24.5 m) are presented. Time series of the HONO photolysis frequency j(HONO) parameterized from the measured NO 2 photolysis frequency at a height of 28 m (orange line and dots), relative humidity and air temperature measured at the canopy top (21 m) from 18 (0:00 CET) to 26 (0:00 CET) Sep. 2007 at the “Waldstein-Weidenbrunnen” research site are presented in the lower panel. During the dry period winds from south-west and south-east were dominating. Temperatures were increasing and daytime RH values were decreasing. The friction velocity (u*), which is a measure for the wind shear and thus wind generated turbulence, was calculated from eddy covariance measurements of horizontal and vertical wind speed. During the dry period u* is lower (especially at night) than before and afterwards. Significant NO values (at the above canopy level) were only measured during day. At the beginning of the dry period, HONO increased continuously with a rate of about 2 ppt h -1 without a pronounced diel cycle, although the 20 Sep. was a clear-sky day with j(HONO) values of about 0.002 s -1 around noon (see Fig. 3). An increasing trend in both the time series of the HONO mixing ratio and the HONO/NO x ratio is evident. To our knowledge this type of accumulation behaviour was not reported so far, and it was not directly linked to an increase of the precursor NO 2 (Fig. 3). Although clarifying the reason for this is far beyond our applied measurement setup, a mechanism that might explain these observations would be the accumulation of (photo-) chemically formed or Appendix C 87 Atmos. Chem. Phys., 11, 841–855, 2011 deposited HONO at the surface and the subsequent release by increasing RH, according to a Langmuir-type surface mechanism proposed by Trick (2004) due to enhanced adsorption of water molecules and subsequent release of HONO, as RH increases in the late afternoon. Additionally, HONO formation due to photolysis of deposited HNO 3 /nitrate as suggested by Zhou et al. (2002a; 2002b; 2003) might be an explanation, since the precursor (“sticky” HNO 3 ) is deposited efficiently to the canopy (Wolff et al., 2010) and accumulates at the needle surface (Zhou et al., 2002a). 3.2.2 S/V ground versus S/V aerosol The needle surface also constitutes the largest fraction of the total ground surface. Therefore, we estimated the ground surface from measurements of the projected plant surface neglecting stem and understory contributions. The average PAI (Plant Area Index) of this forest stand is 5 m 2 m -2 (Staudt et al., 2010). This projected area can be converted to a surface area by multiplying with π for wooden parts (assuming they are round), which contribute about 20 % (5-35 % (Gower et al., 1999)) to the PAI and by a factor of 2.65 derived by Oren et al. (1986) to convert the projected LAI (~ 80% of PAI) to the geometric needle surface. From that simple scheme we derive a total geometric surface of the crown of 13.7 m 2 m -2 (10.6 m 2 m -2 needle surface and 3.6 m 2 m -2 wooden surface). From SODAR (SOund Detection And Ranging) measurements we inferred an average NBL (Nocturnal Boundary Layer) height of 120 m by a steep change in reflectivity of the sound signal. Using this value as an upper limit for the volume (1 m 2 as base area), and for a lower limit ground surface the geometric needle surface of the canopy, we get a S/V ground of 0.1 m -1 , which is an order of magnitude higher than e.g. reported by Yu et al. (2009). However, Yu et al. (2009) took only the inverse of the mixed layer height as S/V ground , not accounting for any roughness of the surface. Especially, during nighttime the boundary layer height represents an upper limit for the volume, because mixing is very limited within the stable thermally stratified NBL (Stull, 1988). Vogel et al. (2003) used a value of 0.1 m -1 to model heterogeneous HONO production in the lowest box of their model but increased S/V ground to 0.3 m -1 , which matched the observations better. This is consistent with our observations that (S/V ground ) 0.1 m -1 reflects a lower limit. In contrast, the S/V ground values given by Lammel and Cape (1996) were an order of magnitude higher, e.g., considering vegetation surfaces with 0.6-1.4 m -1 (for a mixed layer height of 100 m). Additionally, we calculated the aerosol surface from the measured aerosol number size Appendix C 88 Atmos. Chem. Phys., 11, 841–855, 2011 distributions and we found that S/V aerosol was typically less than 1% of S/V ground . Due to the different reactivity and gas diffusivity for ground and aerosol surfaces a direct comparison of S/V ground and S/V aerosol is complicated. Since we measured close to surfaces and these surface areas are about two orders of magnitude larger than the respective aerosol surface for the whole mixed layer, we expect the contribution of aerosol surfaces to HONO formation to be of minor importance in our study. This is in line with measurements from Kleffmann et al. (2003) who found that gradients of HONO were not related to gradients in S/V aerosol . A decrease of the boundary layer height increases S/V ground . Hence, with the same surface more HONO is concentrated in a smaller volume. However, at the same time turbulence is suppressed during these stable conditions. This reduces the exchange between the atmosphere and the surface. 3.2.3 Nighttime HONO conversion frequencies HONO nighttime conversion frequencies F HONO,night from the heterogeneous disproportionation of NO 2 (cf. R1+R2) can be estimated for the dry periods. Su et al. (2008) discussed the problem of different scaling methods for HONO production and suggested to use a combined scaling approach of different quantities emitted close to the ground like black carbon (HONO/BC) or carbon monoxide (HONO/CO), and the “classical" HONO/NO 2 or HONO/NO x . The scaling was (originally) introduced to reduce influences from boundary layer processes such as dilution or vertical mixing. However, local sources of the scaling quantities will affect the ratio (Su et al., 2008). As discussed above, humid surfaces or precipitation will also alter the HONO/NO x ratio due to different solubilities as well as the advection of NO x from road traffic during the morning hours at our site. Due to the lack of carbon monoxide (CO) and black carbon (BC) measurements we use the NO x scaling approach, which was used in many other studies (e.g. Sjödin, 1988; Alicke et al., 2002; Kleffmann et al., 2003;). The approach of Alicke et al. (2002) for inferring conversion frequencies, taking a linear increase of HONO during nighttime divided by the average NO 2 mixing ratio in this time interval is most commonly used. night nightHONO NOtt tHONOtHONO F][)( )]([)]([ 212 12 , − − = (1) Appendix C 89 Atmos. Chem. Phys., 11, 841–855, 2011 There is still no reliable way of inferring HONO conversion frequencies using objective criteria. Yu et al. (2009) used a fixed time interval from 18:00 local time (LT) to midnight (LT) to determine HONO conversion frequencies. This approach leads to a very large scatter in our conversion frequencies. In addition, it yields mainly negative conversion frequencies in the lower height, because HONO increases already before sunset, thus starting at higher levels, and peak mixing ratios are reached before midnight. We also tried an approach different from the “classical” one, not using the maximum HONO mixing ratio as end point but the maximum HONO/NO x ratio that can be regarded as the maximum amount of HONO produced by NO x . 1) Evaluating individual increases of HONO by the “classical” approach, excluding advection events and other disturbances This approach could be used for evaluating data from five nights of the whole IOP (13-25 Sep.) and yielded a value of F HONO, night ± σ = (1.1 ± 0.65) % h -1 for the measurements above the canopy and a value of (0.75 ± 0.45) % h -1 close to the ground. The lower value for the lower height may be caused by choosing the starting point after sunset, whereas the first pronounced increase in HONO mixing ratios occurs already in the hours before sunset. Thus, the starting mixing ratio level is already higher at the lower height, whereas the increase at the upper height normally occurs later and is therefore completely captured. However, the influence of photochemistry has to be excluded for a proper comparison of heterogeneous production, and therefore we cannot use the data before sunset. The values of F HONO,night at both heights agree within their standard deviation (variation over five nights) and are consistent with literature values between 0.4 % h -1 and 1.8 % h -1 recently summarized by Su et al. (2008). The value for the upper height also compares quite well with a value of 1.4 ± 0.4 % h -1 reported by Yu et al. (2009), which was not included in the comparison by Su et al. (2008). 2) Evaluating the period from sunset to the maximum HONO/NO x ratio Conversion frequencies inferred by this approach are identical to the “classical” ones for the conditions during our campaign. The values and the variation of HONO/NO x ratios were mainly correlated to HONO mixing ratios (see Figs. 4 a, b). Therefore, the HONO/NO x maxima occurred simultaneously with the maximum HONO mixing ratios (Fig. 2). Appendix C 90 Atmos. Chem. Phys., 11, 841–855, 2011 Additionally, HONO mixing ratios were nearly independent of its precursor NO 2 (see Fig. 4c). A direct correlation could not be expected since the HONO formation rate dHONO/dt should correlate with NO 2 instead of HONO mixing ratios, due to first order formation of HONO from NO 2 . Nevertheless, assuming similar heterogeneous conversion rates, higher NO 2 values should cause higher HONO values and Fig. 4c should reflect this trend. The lack of this trend was attributed to the fact that in contrast to studies in urban areas low NO 2 mixing ratios were prevailing. About 90 % of the NO 2 values were below 5 ppb and 70 % of the values ranged between 1 and 4 ppb, indicating quite constant NO 2 levels. The highest HONO and HONO/NO x values typically occurred before or around midnight (see Fig. 2) at moderate (2-5 ppb) NO 2 mixing ratios, whereas the highest NO x values occurred in the morning hours (advection from road traffic). The weak correlation of HONO to NO x does not necessarily mean that NO 2 is not a precursor for HONO. We simply do not see a correlation, which is similar to results from another rural forest site (Zhou et al 2002a). This indicates that other processes like deposition or re-emission are also important. Nevertheless, conversion frequencies, as summarized above, are within the range of values reported in literature. Referring to the different conditions and methods used in these experiments, this range is narrow and might provide some guidance for modelling studies. Figure 4. Relationships of HONO and NO x for the measurement height close to the forest floor (0.5m) for the period 13 - 25 September at the “Waldstein-Weidenbrunnen” research site. The upper graphs (a, b) show a better correlation of HONO/NO x to HONO than to 1/NO x , i.e. variations in HONO/NO x are more likely explained by variations in HONO mixing ratios than by NO x values. Appendix C 97 Atmos. Chem. Phys., 11, 841–855, 2011 though HONO lifetimes above and below canopy differ by a factor of 10 to 25 (median values) in the morning (Fig. 6), the difference in HONO mixing ratios is less than 5 ppt (Fig. 7 upper panel) from 10:00 to 12:00 CET, which is within the uncertainty of both instruments. This can be explained by vertical exchange, taking place within the HONO lifetime above canopy. Just after noontime, we observed a pronounced increase of HONO mixing ratios and of HONO/NO x ratios at both heights, with a simultaneous increase of RH by 10%. These patterns were most likely caused by passing clouds, increasing the HONO lifetime by a factor of three (from 9 min to 26 min above canopy, see Fig. 3), but were also related to a change in wind direction. After the noontime peak, HONO mixing ratios decreased again at both heights but with a lower rate below canopy due to the 10 times lower photolysis frequencies. Further increases of lifetime ratios in the afternoon from 25 to 40 may have contributed to the increasing differences. While these differences were counter-balanced by effective vertical mixing, as indicated by a predominantly full coupling of the forest to the air layer above the canopy (C, Cs) in the morning hours, in the afternoon the HONO mixing ratio differences were maintained due to a lack of effective vertical mixing in the decoupled subcanopy (Ds) regime. Thus, only during periods when the subcanopy or even the whole forest are decoupled from the layer above the canopy, the different loss and production processes acting close to the forest floor and in the upper canopy become obvious. We propose a combination of lifetime differences due to shading of the canopy and the intensity of vertical mixing to explain the observed mixing ratio differences during daytime. About two hours before sunset, HONO mixing ratios started to increase at both measurement heights. Above canopy, an increase rate of 40 ppt h -1 led to a slightly higher level of HONO mixing ratios of 70 ± 16 ppt, whereas close to the forest floor, an increase rate of about 90 ppt h -1 resulted in a higher and nearly constant level of about 200 ± 20 ppt. The steep increase in HONO mixing ratios at the ground coincided with an obvious RH increase below canopy, which is not as pronounced as above canopy. After sunset, photolysis no longer affects the atmospheric lifetime of HONO. Thus, the occurrence of different HONO mixing ratios and at the same time different HONO/NO x ratios (about 5 % higher below canopy) at the two measuring heights provide evidence for different HONO-source processes throughout the canopy. The slight increase above the canopy and the strong increase below canopy in the absence of solar radiation and turbulent exchange with the air layer above canopy (Wa) give a strong indication that HONO was formed and released Appendix C 98 Atmos. Chem. Phys., 11, 841–855, 2011 at the ground. We found a good correlation (r² = 0.74) of HONO and RH for the whole period from 16:00 to 20:30 CET close to the forest floor due to accumulation of HONO and humidity below the canopy after decoupling of the forest. Above the canopy the correlation coefficient is very weak (r² = 0.3). Between 20:30 and 21:00 CET a steep increase of HONO mixing ratios was observed. This event is considered to be dominated by an air mass change and not by local HONO production or release, although there are no clear signals in wind speed or direction. But almost all quantities (except NO) changed substantially (see Fig. 3). For example, ozone mixing ratios dropped by about 20 ppb (at 24.5 m), RH increased by 16 % from 20:30 to 22:00 CET and NO x increased from about 2 ppb to 4.5 ppb, which could not be explained by local chemistry alone. Maximum HONO mixing ratios were reached at 21:30 CET with 480 ppt above and 340 ppt below the canopy. This resulted in HONO/NO x ratios of up to 18 % above the canopy. After 21:00 CET, the HONO mixing ratios decreased again at both heights while RH continued to increase. Thus, a negative RH dependence was observed with coefficients of determination of 0.9 at 24.5 m height (RH = 78-85 %) and 0.94 at 0.5 m height (RH = 88-93 %). The slopes are nearly identical but the humidity range is very different. Therefore, it is speculative at best to draw any conclusions about the underlying physical or chemical processes. Although we often found a good correlation of HONO and RH, we could not infer a simple relationship between RH and HONO mixing ratios. One reason for this is that both quantities exhibited a diel cycle that was affected by different (independent) environmental factors, e.g. radiation. HONO was formed near the ground and accumulated during nighttime, whereas RH increased due to cooling of the surface and evaporation still occurring in the afternoon and subsequent accumulation. During daytime HONO was photolyzed, whereas RH decreased due to surface heating although evaporation is enhanced. HONO and RH both decreased during daytime due to dilution by mixing with dryer and HONO depleted air from aloft. The only obvious relation are declining HONO mixing ratios at RHs above 95 % as already observed by Yu et al (2009). 4 Conclusions For the first time, we have measured HONO mixing ratios simultaneously at two heights within and above a forest canopy using interference-corrected wet chemical analyzers (two Appendix C 99 Atmos. Chem. Phys., 11, 841–855, 2011 LOPAP instruments). The instruments agreed within 12 % (2σ) during side-by-side measurements under fair and relatively dry weather periods, allowing for a detailed interpretation of the measured mixing ratio differences. The measured HONO mixing ratios were influenced by a combination of several processes, such as (a) available surface area for heterogeneous formation, (b) co-deposition of species related to HONO formation, (c) HONO desorption from the surface and interaction with RH and (d) turbulent exchange of air masses between the forest and the atmosphere above (coupling). The combination of micrometeorological and chemical measurements allowed us to explain the diel variations of the HONO mixing ratio differences measured below and above a spruce forest canopy. Differences of source or sink processes between above and below canopy became obvious only during periods when they were not overcome by turbulent mixing. For example, rising mixing ratios close to the forest floor in the late afternoon and early night, when the forest canopy was decoupled from the air layer above, provided a clear indication of HONO formation at the ground surface. Higher mixing ratios above the forest canopy in the late night until the morning were in some cases due to advection above the forest, which did only partly penetrate the canopy. In the morning, vertical exchange was most efficient and thus differences in HONO mixing ratios varied around zero despite large differences of photolysis frequencies (factor of 10-25) below and above the canopy. Moreover, we observed a build-up of HONO during dry periods that was not related to a build-up of its precursor NO 2 . We could not infer a simple relationship between RH and HONO mixing ratios. This study particularly demonstrated the strong effect of turbulent vertical transport and the influence of humidity conditions on HONO mixing ratios within and above the forest canopy. Nevertheless, in order to further untangle and quantify all different HONO sources and sinks, additional measurements both in the laboratory and in the field are required. 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Zetzsch 2,3 1 University of Bayreuth, Junior Professorship of Atmospheric Chemistry, Bayreuth, Germany 2 University of Bayreuth, Atmospheric Chemistry Research Laboratory, Bayreuth, Germany 3 Fraunhofer Institute for Toxicology and Experimental Medicine, Hannover, Germany To be submitted to Atmospheric Environment Abstract This study investigates the relation of atmospheric nitrous acid (HONO) and relative humidity (RH) from three different field campaigns. As mixing ratios of HONO and RH co-vary on different timescales, especially considering a pronounced diurnal cycle, we used Singular System Analysis (SSA) to extract signal contributions from these different timescales. By using the advantage of SSA to reconstruct the signal and by choosing only long term trends and variations associated with the diurnal cycle for reconstruction, we were able to extract the residual signal containing the higher frequency contributions. As this residual signal is only about 2 % of the original RH signal and about 20 % of the original HONO signal, identification of processes which couple RH and HONO by correlation studies was not fully convincing. However, other than measured time series, all residual time series (N = 4 pairs) showed a slight positive correlation (r~0.2) of HONO with RH pointing to a potential Appendix D 113 The only obvious feature is the sharp decline of HONO values above 95 % RH as already observed by other groups (e.g. Stutz et al., 2004; Yu et al., 2009). This could be attributed to the formation of liquid films which take up HONO (i.e. “wet” surface) or to rain events (where droplets and liquid films take up HONO). Another result regarding “wet” surfaces was that the removal of HONO values associated with wind directions (140 - 330°) originating from the sea during DOMINO (based on the analysis of Diesch et al., 2011), substantially improved the correlation (see also sect. 3.3). These values were presumably influenced by the equilibrium with the sea surface as proposed by Wojtal et al. (2010). Figure 2: Night-time data of HONO versus RH are shown on the left hand side. The colour code denotes the level of the NO 2 precursor. The NO 2 levels are categorized by low (< 1.1 ppb = 25 percentile), middle (1.1 - 3.2 / 25 - 75 percentile) and high (> 3.2 = 75 percentile) values. On the right hand side, HONO values versus NO 2 values are shown for the same data. Another influence on the relation of HONO and RH might arise from variations of the HONO precursor NO 2 , but correlations of HONO and NO 2 were weak as well, especially for IOP I (r² = 0.14 at 24 m and r² = 0.05 at 0.5 m). The low or non-existent correlation of HONO with its precursor NO 2 was the starting point to think about the influence of RH. Although correlations of HONO with NO 2 were higher for IOP II and especially good for DOMINO (r² = 0.44), the NO 2 levels reflect more a tendency (higher NO 2 <=> higher HONO) than a strong correlation (Fig. 2). This might be attributed to the rather slow formation of HONO from NO 2 (max. 2 % h -1 ). This means that HONO mixing ratios build up slowly and are thus more evenly distributed, whereas NO 2 values might be quite variable as indicated by the results of Pöhler (2010) and Harrison et al. (1996). Therefore, HONO values were not normalized to NO 2 to avoid disturbances of the HONO to RH correlation due to variations in NO 2 . Usually, normalization to NO 2 is done to account for changes in boundary layer height and precursor concentration. Some shortcomings of this scaling approach have already been Appendix D 114 discussed by Su et al. (2008 a). Furthermore, correlations on different timescales can be found by applying the SSA, and may possibly allow for a separation of HONO and NO 2 and HONO and RH correlations. The SSA (e.g. Elsner and Tsonis, 1996) was chosen for this analysis as it provides the opportunity to reconstruct the time series by using signal contributions associated with certain timescales (oscillations). This is necessary to remove the signal contributions of the diurnal cycle of HONO and RH and the long term trends in order to identify correlations on shorter timescales which might be a hint at the interaction with fast physical processes (adsorption/desorption). Appendix D 115 3.2 Dominant frequencies in HONO and RH time series and their contribution to the signals As described in detail in sect. 2.1, the embedded time series of HONO and RH are decomposed into eigenvectors and eigenvalues by means of single value decomposition (SDV). The eigenvalues are ordered by their decreasing rank (i.e. signal contribution). A Fourier-transform of the eigenvectors yields their dominant frequency respectively dominant periodic time τ . Figure 3: Dominant periodic times ( τ ) in hours of the eigenvectors for different window lengths (L) ordered by decreasing rank of the eigenvalues for the HONO time series measured at 0.5 m during IOP I. As the window length is the only free parameter to choose for the SSA, Fig. 3 shows the dependency of the frequency of the 25 leading EOFs (ordered by decreasing rank, i.e. contribution to the signal) for different window lengths. The time series “IOPI 0.5 m” included N t = 1585 points (10 min average values). Thus, window lengths from 300 to 700 equal N t /5 to nearly N t /2, which corresponds to 50 to 116 hours (Fig. 2, “long term trends”). Appendix D 116 For all chosen window lengths, the first leading EOF has the window length as dominant periodic time ( τ ) which consists of the mean value respectively mean trend. For L close to 24 hours (50-67 hours; L=300 and L=400) the next important periodic time is the diurnal cycle. In contrast, for longer window lengths, the first three EOFs represent long term signals. The whole time-series under study is limited to 11 days. Therefore, oscillations due to high and low pressure regimes (~ 7 days) identified visually by Sörgel et al. (2011a) in this time series are not captured by SSA. Due to its good water solubility, HONO is washed out by rain and increases during the dry periods (Sörgel et al., 2011a). These strong forcing mechanisms, which are not resolved by the maximum possible L, may be the reason for the dominance of eigenvectors with the main frequency being the window length. The diurnal cycle is identified as an important signal contribution independent of the choice of L, thus providing confidence for further analysis after subtraction of the diurnal cycle. We chose the maximum L=700 for further analysis of all time series. Figure 4: Dominant periodic times ( τ ) in hours of the eigenvectors (ordered by decreasing rank) of RH (open circles) and HONO (red triangles) from IOP I close to the forest floor. Figure 4 shows the τ values of the first 25 EOFs of HONO and RH (which contribute about 80-90 % to the HONO signal and 98 % to the RH signal, cf. Fig. 5). The first 10 EOFs are almost identical and represent the long term trends and the diurnal cycle. The pairs of Appendix D 117 eigenvectors with the same frequency denote oscillations (Vautard and Ghil, 1989; Elsner and Tsonis, 1996; Ghil et al., 2002) which can be harmonic or anharmonic. These co-variations can be attributed to a real cause (wash out by rain) on the longer timescales, but the diurnal cycles of both might be simply co-variations due to solar radiation. The diurnal cycle of RH is mainly driven by the temperature, with lower values at higher temperatures during the day and increasing values during cooling at night. For HONO the photolysis (UV part of solar radiation) is the most important sink during the day, whereas HONO accumulates at night. As humidity mainly originates from evaporation (soil) or evapotranspiration (plants) and previous studies suggest HONO formation at the ground (e.g. Febo and Perrino, 1996; Harrison et al., 1996; Zhang et al., 2009; Sörgel et al., 2011a,b; Wong et al., 2011), both RH and HONO mixing ratios are sensitive to the mixing height (dilution by vertical mixing). The mixing height and thus vertical turbulent diffusion respectively exhibit a diurnal cycle as well. Therefore, HONO and RH are expected to be correlated at the timescale of the diurnal cycle but not necessarily due to interaction of chemical or physical processes. Figure 5: Cumulative signal contribution in % of the first 100 EOFs of HONO and RH for all campaigns. As the sum of all eigenvalues represents the total variance of the original time series one can express the signal contribution of the EOFs by the contribution of their associated eigenvalues to the total variance (Vautard and Ghil, 1989). A first obvious difference between HONO and RH time series is the amount of variance explained by the leading EOFs (Fig.5). The first EOF, which consists of the mean and long term trends, comprises more than 98 % of the RH signal but only about 50 to 80 % of the HONO signal. This might be attributed to the Appendix D 118 statistical distributions of the underlying data sets. HONO values are log-normal distributed whereas RH values are more or less normal or bimodal distributed. Figure 5 shows the eigenvalues contribution in % of the total sum of eigenvalues (= 100 %) plotted by decreasing rank. This can be interpreted similar to a so called “scree-diagram”, where the eigenvalues themselves are plotted by decreasing rank (i.e. decreasing value). The “scree-diagram” can be used for a first simple separation into the EOFs comprising the signal and those for the noise. Typically, the signal part is assigned to the first EOFs before the break in the slope (looks like a hockey stick) of the “scree-diagram”, but especially for larger L (as used in this study) there might be no “noise floor” and the break is smoothed (Vautard and Ghil, 1989). The break for RH values already occurs after three EOFs whereas for HONO the break occurs after eight EOFs. However, apparently the first 20 to 30 eigenvalues still contain signal information. 3.3 Correlations in the signals after subtraction of diurnal and long term contributions In order to extract possible correlations of HONO and RH apart from the diurnal cycle and the long term trends, which are the dominant signal contributions (Fig. 4 and 5 sect. 3.2), we subtract these contributions from the original time series. Figure 6 (upper panel) shows the measured time series and the reconstructed time series of HONO and RH for IOP I 0.5m using the first 11 EOFs. Using all 700 EOFs, the reconstruction would be identical to the original time series, but this complete reconstruction would be very computational intensive. Appendix D 119 Fig. 6: Upper left panel: HONO measured (black dots) and reconstructed time series (red line) using the first 11 EOFs; upper right panel: measured RH (black dots) and reconstructed time series (red line) using the first 11 EOFs; lower panels: residuals after subtracting the reconstructed time series from the measured ones for HONO (left) and RH (right). The lower panel of Fig. 6 shows the residuals after subtracting the reconstructed time series containing long term trends and the diurnal cycle. For both HONO and RH, trends are efficiently removed. Also, oscillations in the residuals have higher frequencies than the diurnal cycle, thus also proving that the diurnal cycle has been removed and higher frequency contributions remain. Only during the rainy periods (18 th to 20 th September) with almost constant RH and HONO values this method induced a diurnal cycle by restricting the reconstruction to the first EOFs (Fig. 6). Table 1 contains the correlation coefficients of the measured time series (“original”), the residuals after subtracting long term and diurnal contributions (“orig-RC”), and the residuals from subtracting the RCs not from the original time series, but also from a reconstructed one with higher frequency contributions (first 20 to 30 EOFs) which is equivalent to reconstruct RC11 to RC30 (“RC-RC”). Appendix D 120 Table 1: Correlation coefficients (according to Pearson and Spearman) of the measured time series of HONO and RH (“original”), the residuals after subtracting the reconstructed time series (“orig-RC”) and the residuals using not the original time series but a reconstructed time series with higher frequency contributions (“RC-RC”). Correlation coefficient IOP I 0.5m IOP I 24m IOP II DOMINO original (Spearman) 0.009 0.234 0.467 -0.042 0.74* orig-RC (Pearson) 0.163 0.225 0.309 0.189 orig-RC (Spearman) 0.163 0.270 0.311 0.206 RC-RC (Pearson) 0.021 0.131 0.192 0.224 RC-RC (Spearman) -0.003 0.148 0.243 0.268 * Correlation coefficient derived excluding values with marine influence. As described in sect. 3.1, HONO values were log-normal distributed and RH values were normal or bimodal distributed. Therefore, for the original time series only the rank correlation (Spearman) coefficients are given. The values are quite variable (~ -0.04 to 0.47). The poor correlations obtained for the DOMINO data were presumably caused by the equilibrium of HONO with the sea surface as proposed by Wojtal et al. (2010), as the correlation improved substantially to 0.74 by removing values associated with marine air masses. Correlations of the residuals after subtraction (“orig-RC”) of long term and diurnal contributions are less variable (0.16 - 0.31) and thus all slightly positive correlated. An attempt to improve the correlation by reducing the contribution of noise (“RC-RC”) did not result in higher correlation coefficients. All correlations for “RC-RC” except for “DOMINO” are lower than the correlations of the original time series and that of the residuals "orig-RC". This attempt was based on the visual inspection of the “scree diagram” (see section 3.2), and it was first concluded from the visual inspection that the first 20 to 30 EOFs still contain signal. This is possibly not the case, and relatively large signal contributions (between EOF 11 and EOF 30) are noise components. Thus, it remains to be solved by using more sophisticated tools like Monte-Carlo-SSA (e.g. Allen and Smith, 1996) whether the correlations are weak due to the influence of noise, or if the noise causes the correlation. With this method the significance of oscillations or signal contributions can be tested against a noise model (white noise and coloured noise, e.g. Allen and Smith., 1996). This would help to create a set of RCs which contain signal information alone and use them for reconstruction. Appendix D 121 Furthermore, the interaction of HONO and RH on timescales shorter than the diurnal variation is not necessarily a linear (Pearson) or a monotonic function (Spearman). On the other hand, the heterogeneous formation reaction of HONO from the disproportionation of NO 2 has a first order dependence on water vapour (e.g. Sakamaki et al., 1983). But this reaction should be also sensitive to NO 2 . Possibly, the better correlations of HONO and RH as well as HONO and NO 2 during DOMINO and IOP II were caused by the prevailing dry weather and thus more values under “dry surface” conditions. Under the dry surface conditions HONO and RH are expected to be positively correlated due to a) first order dependence of heterogeneous HONO formation on water vapour (e.g. Sakamaki et al. 1983), b) possibly due to HONO replacement by co-adsorption of water (Trick, 2004; Stutz, 2005). Furthermore, if we only take the dry period of IOP I 0.5m (i.e. from 20th to 25th of Sept.; cf. Fig. 5) the correlation coefficients improve from 0.16 to 0.34 (Spearman), which is close to the values of the summer campaign “IOP II”. 4 Conclusions In contrast to other studies, which can be interpreted as an attempt to estimate the influence of RH on the modulation of the amplitude of the diurnal cycle of HONO (e.g. Stutz et al., 2004; Yu et al., 2009), we tried to identify the interactions of HONO and RH on shorter timescales. To achieve this goal, the diurnal cycle and the long term trends have to be removed from the signal. SSA has been shown to successfully detect long term trends and the signal contributions of the diurnal cycle in HONO and RH time series. Therefore, it was possible to reconstruct the time series from the signal contributions of the diurnal cycle and the long term trends by subtracting it from the measured one (original). Trends and the diurnal cycle were efficiently removed by this method, and the residuals where slightly positive correlated for all-time series. Unfortunately, identification of correlations which are an indicator (but not a proof) for underlying processes in the remaining signal (2-20 %) was quite a challenge. There might be several reasons: - the remaining signal also contains noise contributions, which might be resolved by a better separation of signal and noise by more sophisticated tools like Monte Carlo SSA - the amplitude of the HONO signal is modulated by RH instead of the signal directly depending on RH Appendix D 122 - other parameters like the HONO precursor NO 2 are more important for the variations than RH alone - time resolution of the instruments (about 10 min for HONO) is still too low to resolve these processes - precision of the HONO measurements (about 12 %) is still too low The overall picture of the relation of HONO and RH was found to fit quite well to the scheme of surface wetness proposed by Lammel (1999) as “dry” (< 55 % RH), “partially wet” (55 – 90 % RH) and “wet” (> 90 % RH). Below 60/65 % RH correlations of HONO and RH were high, but consist of a few (< 100) data points only. For DOMINO a good correlation was obtained only after removing values from marine air masses. The mostly lower HONO values were presumably caused by the equilibrium with the sea surface as proposed by Wojtal et al. (2010). In the RH range of the “dry” surface the first order dependence of HONO formation on water vapour (e.g. Sakamaki et al., 1983) and the displacement of HONO from the surface by co-adsorbing water (proposed by Trick, 2004; Stutz, 2005) are proposed to cause the correlation. The “partially wet” surfaces denote a mixture of processes, as “dry” and wet surfaces coexist. The “partially wet” surfaces are caused by deliquescing salts. They can either release or take up HONO depending on ionic strength and pH (Becker et al., 1996; Becker et al. 1998; Hirokawa et al., 2008). This might be responsible for the high variability of HONO in the corresponding RH range (~70 to 95 %), which is accompanied with low correlations to both RH and NO 2 . A rather clear result denotes the “wet” surfaces as HONO values drop above 95 % RH. This has already been observed by other groups (e.g. Stutz et al., 2004; Yu et al., 2009). Another point, which should be considered in future studies, was that HONO values were lognormal distributed, whereas RH values were normal or bimodal distributed. Thus, for a standard (Pearson) correlation the log (HONO) values have to be taken or the rank correlation coefficient (Spearman) has to be used instead. Thus, SSA has proven to be a useful tool to extract signal contributions aside the diurnal cycle, which is important for atmospheric chemistry. Nevertheless, further investigation of the noisy residuals needs more sophisticated statistical tools or higher frequency and less noisy time series. Acknowledgements: The authors gratefully acknowledge financial support by the German Science Foundation (DFG projects ZE 792/4-1 and HE 5214/4-1). We are grateful to Michael Hauhs, Chair of Ecological Modelling, University of Bayreuth, for drawing our attention to