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Biomass burning (BB) is a major source of reactive organic carbon into the atmosphere. Once in the atmosphere, these organic BB emissions, in both the gas and particle phases, are subject to atmospheric oxidation, though the nature and impact of the chemical transformations are not currently well constrained. Here we describe experiments carried out as part of the FIREX FireLab campaign, in which smoke from the combustion of fuels typical of the Western US was sampled into an environmental chamber and exposed to high concentrations of OH, to simulate the equivalent of up to two days of atmospheric oxidation. The evolution of the organic mixture was monitored using three real-time time-of-flight mass spectrometric instruments (a proton transfer reaction mass spectrometer, an iodide chemical ionization mass spectrometer, and an aerosol mass spectrometer), providing measurements of both individual species and ensemble properties of the mixture. The combined measurements from these instruments achieve a reasonable degree of carbon closure (within 15–35 %), indicating that most of the reactive organic carbon is measured by these instruments. Consistent with our previous studies of the oxidation of individual organic species, atmospheric oxidation of the complex organic mixture leads to the formation of species that on average are smaller and more oxidized than those in the unoxidized emissions. In addition, comparison of mass spectra from the different fuels indicates that the oxidative evolution of BB emissions proceeds largely independent of fuel type, with different fresh smoke mixtures ultimately converging into a common, aged distribution of gas-phase compounds. This distribution is characterized by high concentrations of several small, volatile oxygenates, formed from fragmentation reactions, as well as a complex pool of many minor oxidized species and secondary organic aerosol, likely formed via functionalization processes.

The atmospheric oxidation of dimethyl sulfide (DMS) represents a major natural source of atmospheric sulfate aerosols. However, there remain large uncertainties in our understanding of the underlying chemistry that governs the product distribution and sulfate yield from DMS oxidation. Here, chamber experiments were conducted to simulate gas-phase OH-initiated oxidation of DMS under a range of reaction conditions. Most importantly, the bimolecular lifetime (τbi) of the peroxy radical CH3SCH2OO was varied over several orders of magnitude, enabling the examination of the role of peroxy radical isomerization reactions on product formation. An array of analytical instruments was used to measure nearly all sulfur-containing species in the reaction mixture, and results were compared with a near-explicit chemical mechanism. When relative humidity was low, “sulfur closure” was achieved under both high-NO (τbi<0.1 s) and low-NO (τbi>10 s) conditions, though product distributions were substantially different in the two cases. Under high-NO conditions, approximately half the product sulfur was in the particle phase, as methane sulfonic acid (MSA) and sulfate, with most of the remainder as SO2 (which in the atmosphere would eventually oxidize to sulfate or be lost to deposition). Under low-NO conditions, hydroperoxymethyl thioformate (HPMTF, HOOCH2SCHO), formed from CH3SCH2OO isomerization, dominates the sulfur budget over the course of the experiment, suppressing or delaying the formation of SO2 and particulate matter. The isomerization rate constant of CH3SCH2OO at 295 K is found to be 0.13±0.03 s−1, in broad agreement with other recent laboratory measurements. The rate constants for the OH oxidation of key first-generation oxidation products (HPMTF and methyl thioformate, MTF) were also determined (kOH+HPMTF=2.1×10-11 cm3 molec.−1 s−1, kOH+MTF=1.35×10-11 cm3 molec.−1 s−1). Product measurements agree reasonably well with mechanistic predictions in terms of total sulfur distribution and concentrations of most individual species, though the mechanism overpredicts sulfate and underpredicts MSA under high-NO conditions. Lastly, results from high-relative-humidity conditions suggest efficient heterogenous loss of at least some gas-phase products.

India experiences some of the highest levels of ambient PM2.5 aerosol pollution in the world. However, due to the historical dearth of in situ measurements, chemical transport models that are often used to estimate PM2.5 exposure over the region are rarely evaluated. Here, we conduct a novel model comparison with speciated airborne measurements of fine aerosol, revealing large biases in the ammonium and nitrate simulations. To address this, we incorporate process-level changes to the model and use satellite observations from the Cross-track Infrared Sounder (CrIS) and the TROPOspheric Monitoring Instrument (TROPOMI) to constrain ammonia and nitrogen oxide emissions. The resulting simulation demonstrates significantly lower bias (NMBModified: 0.19; NMBBase: 0.61) when validated against the airborne aerosol measurements, particularly for the nitrate (NMBModified: 0.08; NMBBase: 1.64) and ammonium simulation (NMBModified: 0.49; NMBBase: 0.90). We use this validated simulation to estimate a population-weighted annual PM2.5 exposure of 61.4 μg m-3, with the RCO (residential, commercial, and other) and energy sectors contributing 21% and 19%, respectively, resulting in an estimated 961,000 annual PM2.5-attributable deaths. Regional exposure and sectoral source contributions differ meaningfully in the improved simulation (compared to the baseline simulation). Our work highlights the critical role of speciated observational constraints in developing accurate model-based PM2.5 aerosol source attribution for health assessments and air quality management in India.

Biomass burning organic aerosol (BBOA) in the atmosphere contains many compounds that absorb solar radiation, called brown carbon (BrC). While BBOA is in the atmosphere, BrC can undergo reactions with oxidants such as ozone which decrease absorbance, or whiten. The effect of temperature and relative humidity (RH) on whitening has not been well constrained, leading to uncertainties when predicting the direct radiative effect of BrC on climate. Using an aerosol flow-tube reactor, we show that the whitening of BBOA by oxidation with ozone is strongly dependent on RH and temperature. Using a poke-flow technique, we show that the viscosity of BBOA also depends strongly on these conditions. The measured whitening rate of BrC is described well with the viscosity data, assuming that the whitening is due to oxidation occurring in the bulk of the BBOA, within a thin shell beneath the surface. Using our combined datasets, we developed a kinetic model of this whitening process, and we show that the lifetime of BrC is 1 d or less below ∼1 km in altitude in the atmosphere but is often much longer than 1 d above this altitude. Including this altitude dependence of the whitening rate in a chemical transport model causes a large change in the predicted warming effect of BBOA on climate. Overall, the results illustrate that RH and temperature need to be considered to understand the role of BBOA in the atmosphere.

Fires emit a substantial amount of non-methane organic gases (NMOGs), the atmospheric oxidation of which can contribute to ozone and secondary particulate matter formation. However, the abundance and reactivity of these fire NMOGs are uncertain and historically not well constrained. In this work, we expand the representation of fire NMOGs in a global chemical transport model, GEOS-Chem. We update emission factors to Andreae (2019) and the chemical mechanism to include recent aromatic and ethene and ethyne model improvements (Bates et al., 2021; Kwon et al., 2021). We expand the representation of NMOGs by adding lumped furans to the model (including their fire emission and oxidation chemistry) and by adding fire emissions of nine species already included in the model, prioritized for their reactivity using data from the Fire Influence on Regional to Global Environments (FIREX) laboratory studies. Based on quantified emissions factors, we estimate that our improved representation captures 72 % of emitted, identified NMOG carbon mass and 49 % of OH reactivity from savanna and temperate forest fires, a substantial increase from the standard model (49 % of mass, 28 % of OH reactivity). We evaluate fire NMOGs in our model with observations from the Amazon Tall Tower Observatory (ATTO) in Brazil, Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) and DC3 in the US, and Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) in boreal Canada. We show that NMOGs, including furan, are well simulated in the eastern US with some underestimates in the western US and that adding fire emissions improves our ability to simulate ethene in boreal Canada. We estimate that fires provide 15 % of annual mean simulated surface OH reactivity globally, as well as more than 75 % over fire source regions. Over continental regions about half of this simulated fire reactivity comes from NMOG species. We find that furans and ethene are important globally for reactivity, while phenol is more important at a local level in the boreal regions. This is the first global estimate of the impact of fire on atmospheric reactivity.

Nitrogen oxides (NOx) play a key role in regulating the oxidizing capacity of the atmosphere through controlling the abundance of O3, OH, and other important gas and particle species. Some recent studies have suggested that particulate nitrate, which is conventionally considered as the ultimate oxidation product of NOx, can undergo "renoxification" via photolysis, recycling NOx and HONO back to the gas phase. However, there are large discrepancies in estimates of the importance of this channel, with reported renoxification rate constants spanning three orders of magnitude. In addition, previous laboratory studies derived the rate constant using bulk particle samples collected on substrates instead of suspended particles. In this work, we study renoxification of suspended submicron particulate sodium and ammonium nitrate through controlled laboratory photolysis experiments using an environmental chamber. We find that, under atmospherically relevant wavelengths and relative humidities, particulate inorganic nitrate releases NOx and HONO less than 10 times as rapidly as gaseous nitric acid, putting our measurements on the low end of recently reported renoxification rate constants. To the extent that our laboratory conditions are representative of the real atmosphere, renoxification from the photolysis of inorganic particulate nitrate appears to play a limited role in contributing to the NOx and OH budgets in remote environments. These results are based on simplified model systems; future studies should investigate renoxification of more complex aerosol mixtures that represent a broader spectrum of aerosol properties to better constrain the photolysis of ambient aerosols.

India experiences some of the highest levels of ambient PM

Despite the central role of reactive organic carbon (ROC) in the formation of secondary species that impact global air quality and climate, our assessment of ROC abundance and impacts is challenged by the diversity of species that contribute to it. We revisit measurements of ROC species made during two field campaigns in the United States: the 2013 SOAS campaign in forested Centreville, AL, and the 2010 CalNex campaign in urban Pasadena, CA. We find that average measured ROC concentrations are about twice as high in Pasadena (73.8 μgCsm-3) than in Centreville (36.5 μgCsm-3). However, the OH reactivity (OHR) measured at these sites is similar (20.1 and 19.3 s-1). The shortfall in OHR when summing up measured contributions is 31%, at Pasadena and 14% at Centreville, suggesting that there may be a larger reservoir of unmeasured ROC at the former site. Estimated O3 production and SOA potential (defined as concentration × yield) are both higher during CalNex than SOAS. This analysis suggests that the ROC in urban California is less reactive, but due to higher concentrations of oxides of nitrogen and hydroxyl radicals, is more efficient in terms of O3 and SOA production, than in the forested southeastern U.S.

Fifty years ago, Levy identified hydroxyl radicals as the driver of chemistry in the troposphere

Aerosol indirect radiative forcing (IRF), which characterizes how aerosols alter cloud formation and properties, is very sensitive to the preindustrial (PI) aerosol burden. Dimethyl sulfide (DMS), emitted from the ocean, is a dominant natural precursor of non-sea-salt sulfate in the PI and pristine present-day (PD) atmospheres. Here we revisit the atmospheric oxidation chemistry of DMS, particularly under pristine conditions, and its impact on aerosol IRF. Based on previous laboratory studies, we expand the simplified DMS oxidation scheme used in the Community Atmospheric Model version 6 with chemistry (CAM6-chem) to capture the OH-addition pathway as well as the H-abstraction pathway and the associated isomerization branch. These additional oxidation channels of DMS produce several stable intermediate compounds, e.g., methanesulfonic acid (MSA) and hydroperoxymethyl thioformate (HPMTF), delay the formation of sulfate, and hence, alter the spatial distribution of sulfate aerosol and radiative impacts. The expanded scheme improves the agreement between modeled and observed concentrations of DMS, MSA, HPMTF, and sulfate over most marine regions based on the NASA Atmospheric Tomography (ATom), the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA), and the VAMOS Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx) measurements. We find that the global HPMTF burden, as well as the burden of sulfate produced from DMS oxidation are relatively insensitive to the assumed isomerization rate, but the burden of HPMTF is very sensitive to a potential additional cloud loss. We find that global sulfate burden under PI and PD emissions increase to 412 Gg-S (+29 %) and 582 Gg-S (+8.8 %), respectively, compared to the standard simplified DMS oxidation scheme. The resulting annual mean global PD direct radiative effect of DMS-derived sulfate alone is −0.11 W m−2. The enhanced PI sulfate produced via the gas-phase chemistry updates alone dampens the aerosol IRF as anticipated (−2.2 W m−2 in standard versus −1.7 W m−2 with updated gas-phase chemistry). However, high clouds in the tropics and low clouds in the Southern Ocean appear particularly sensitive to the additional aqueous-phase pathways, counteracting this change (−2.3 W m−2). This study confirms the sensitivity of aerosol IRF to the PI aerosol loading, as well as the need to better understand the processes controlling aerosol formation in the PI atmosphere and the cloud response to these changes.

Stay-at-home policies invoked in response to COVID-19 have led to well-publicized drops in some air pollutants. The extent to which such reductions translate to improved air quality is dictated by not only emissions and meteorology, but also chemical transformations in the atmosphere.

The loss of ozone to terrestrial and aquatic systems, known as dry deposition, is a highly uncertain process governed by turbulent transport, interfacial chemistry, and plant physiology. We demonstrate the value of using Deep Neural Networks (DNN) in predicting ozone dry deposition velocities. We find that a feedforward DNN trained on observations from a coniferous forest site (Hyytiala, Finland) can predict hourly ozone dry deposition velocities at a mixed forest site (Harvard Forest, Massachusetts) more accurately than modern theoretical models, with a reduction in the normalized mean bias (0.05 versus similar to 0.1). The same DNN model, when driven by assimilated meteorology at 2 degrees x 2.5 degrees spatial resolution, outperforms the Wesely scheme as implemented in the GEOS-Chem model. With more available training data from other climate and ecological zones, this methodology could yield a generalizable DNN suitable for global models. Plain Language Summary Ozone in the lower atmosphere is a toxic pollutant and greenhouse gas. In this work, we use a machine learning technique known as deep learning, to simulate the loss of ozone to Earth's surface. We show that our deep learning simulation of this loss process outperforms existing traditional models and demonstrate the opportunity for using machine learning to improve our understanding of the chemical composition of the atmosphere.

Significance Poor air quality is a global public health issue, contributing to millions of premature deaths per year worldwide. Low-cost air quality sensors are a promising tool to improve monitoring capabilities. In this study, we built and deployed a low-cost sensor network for emergency response during an extreme air quality event, the 2018 Kīlauea Lower East Rift Zone eruption. This network was used to estimate fine-scale population exposures to multiple pollutants, to measure the chemical transformation of volcanic emissions, and to provide real-time observations as part of emergency management efforts., Extreme air quality episodes represent a major threat to human health worldwide but are highly dynamic and exceedingly challenging to monitor. The 2018 Kīlauea Lower East Rift Zone eruption (May to August 2018) blanketed much of Hawai‘i Island in “vog” (volcanic smog), a mixture of primary volcanic sulfur dioxide (SO2) gas and secondary particulate matter (PM). This episode was captured by several monitoring platforms, including a low-cost sensor (LCS) network consisting of 30 nodes designed and deployed specifically to monitor PM and SO2 during the event. Downwind of the eruption, network stations measured peak hourly PM2.5 and SO2 concentrations that exceeded 75 μg m−3 and 1,200 parts per billion (ppb), respectively. The LCS network’s high spatial density enabled highly granular estimates of human exposure to both pollutants during the eruption, which was not possible using preexisting air quality measurements. Because of overlaps in population distribution and plume dynamics, a much larger proportion of the island’s population was exposed to elevated levels of fine PM than to SO2. Additionally, the spatially distributed network was able to resolve the volcanic plume’s chemical evolution downwind of the eruption. Measurements find a mean SO2 conversion time of ∼36 h, demonstrating the ability of distributed LCS networks to observe reaction kinetics and quantify chemical transformations of air pollutants in a real-world setting. This work also highlights the utility of LCS networks for emergency response during extreme episodes to complement existing air quality monitoring approaches.

Atmospheric aerosol over the North Atlantic Ocean impacts regional clouds and climate. In this work, we use a set of sun photometer observations of aerosol optical depth (AOD) located on the Graciosa and Cape Verde islands, along with the GEOS‐Chem chemical transport model to investigate the sources of these aerosol and their transport over the North Atlantic Ocean. At both locations, the largest simulated contributor to aerosol extinction is the local source of sea‐salt aerosol. In addition to this large source, we find that signatures consistent with long‐range transport of anthropogenic, biomass burning, and dust emissions are apparent throughout the year at both locations. Model simulations suggest that this signal of long‐range transport in AOD is more apparent at higher elevation locations; the influence of anthropogenic and biomass burning aerosol extinction is particularly pronounced at the height of Pico Mountain, near the Graciosa Island site. Using a machine learning approach, we further show that simulated observations at these three sites (near Graciosa, Pico Mountain, and Cape Verde) can be used to predict the simulated background aerosol imported into cities on the European mainland, particularly during the local winter months, highlighting the utility of background AOD monitoring for understanding downwind air quality., Key Points Sun photometer AOD observations at island sites characterize long‐range transport of aerosol species across the North Atlantic OceanModel simulations suggest that higher elevation observations provide a pronounced signature of anthropogenic and biomass burning aerosolThese island sites contain information useful for predicting imported aerosol to the European continent

Desert dust aerosols affect Earth’s global energy balance through interactions with radiation(1,2), clouds(3,4), and ecosystems(5). But the magnitudes of these effects are so uncertain that it remains unclear whether atmospheric dust has a net warming or cooling effect on global climate(1,4,6). Consequently, it is still uncertain whether large changes in atmospheric dust loading over the past century have slowed or accelerated anthropogenic climate change(4,7–9), and the climate impact of possible future alterations in dust loading is similarly disputed(9,10). Here we use an integrative analysis of dust aerosol sizes and abundance to constrain the climatic impact of dust through direct interactions with radiation. Using a combination of observational, experimental, and model data, we find that atmospheric dust is substantially coarser than represented in current climate models. Since coarse dust warms global climate, the dust direct radiative effect (DRE) is likely less cooling than the ~0.4 W/m(2) estimated by models in a current ensemble(2,11–13). We constrain the dust DRE to - 0.20 (−0.48 to +0.20) W/m(2), which suggests that the dust DRE produces only about half the cooling that current models estimate, and raises the possibility that dust DRE is actually net warming the planet.

Bioaerosols are produced by biological processes and directly emitted into the atmosphere, where they contribute to ice nucleation and the formation of precipitation. Previous studies have suggested that fungal spores constitute a substantial portion of the atmospheric bioaerosol budget. However, our understanding of what controls the emission and burden of fungal spores on the global scale is limited. Here, we use a previously unexplored source of fungal spore count data from the American Academy of Allergy, Asthma, and Immunology (AAAAI) to gain insight into the drivers of their emissions. First, we derive emissions from observed concentrations at 66 stations by applying the boundary layer equilibrium assumption. We estimate an annual mean emission of 62 ± 31 m−2 s−1 across the USA. Based on these pseudo-observed emissions, we derive two models for fungal spore emissions at seasonal scales: a statistical model, which links fungal spore emissions to meteorological variables that show similar seasonal cycles (2 m specific humidity, leaf area index and friction velocity), and a population model, which describes the growth of fungi and the emission of their spores as a biological process that is driven by temperature and biomass density. Both models show better skill at reproducing the seasonal cycle in fungal spore emissions at the AAAI stations than the model previously developed by Heald and Spracklen (2009) (referred to as HS09). We implement all three emissions models in the chemical transport model GEOS-Chem to evaluate global emissions and burden of fungal spore bioaerosol. We estimate annual global emissions of 3.7 and 3.4 Tg yr−1 for the statistical model and the population model, respectively, which is about an order of magnitude lower than the HS09 model. The global burden of the statistical and the population model is similarly an order of magnitude lower than that of the HS09 model. A comparison with independent datasets shows that the new models reproduce the seasonal cycle of fluorescent biological aerosol particles (FBAP) concentrations at two locations in Europe somewhat better than the HS09 model, although a quantitative comparison is hindered by the ambiguity in interpreting measurements of fluorescent particles. Observed vertical profiles of FBAP show that the convective transport of spores over source regions is captured well by GEOS-Chem, irrespective of which emission scheme is used. However, over the North Atlantic, far from significant spore sources, the model does not reproduce the vertical profiles. This points to the need for further exploration of the transport, cloud processing, and wet removal of spores. In addition, more long-term observational datasets are needed to assess whether drivers of seasonal fungal spore emissions are similar across continents and biomes.

We discuss the three main areas of new understanding that emerged from the meeting.

Surface ozone (O3) pollution levels are strongly correlated with daytime surface temperatures, especially in highly polluted regions. This correlation is nonlinear and occurs through a variety of temperature-dependent mechanisms related to O3 precursor emissions, lifetimes, and reaction rates, making the reproduction of temperature sensitivities – and the projection of associated human health risks – a complex problem. Here we explore the summertime O3–temperature relationship in the United States and Europe using the chemical transport model GEOS-Chem. We remove the temperature dependence of several mechanisms most frequently cited as causes of the O3–temperature “climate penalty”, including PAN decomposition, soil NOx emissions, biogenic volatile organic compound (VOC) emissions, and dry deposition. We quantify the contribution of each mechanism to the overall correlation between O3 and temperature both individually and collectively. Through this analysis we find that the thermal decomposition of PAN can explain, on average, 20 % of the overall O3–temperature correlation in the United States. The effect is weaker in Europe, explaining 9 % of the overall O3–temperature relationship. The temperature dependence of biogenic emissions contributes 3 % and 9 % of the total O3–temperature correlation in the United States and Europe on average, while temperature-dependent deposition (6 % and 1 %) and soil NOx emissions (10 % and 7 %) also contribute. Even considered collectively these mechanisms explain less than 46 % of the modeled O3–temperature correlation in the United States and 36 % in Europe. We use commonality analysis to demonstrate that covariance with other meteorological phenomena such as stagnancy and humidity can explain the bulk of the remainder of the O3–temperature correlation. Thus, we demonstrate that the statistical correlation between O3 and temperature alone may greatly overestimate the direct impacts of temperature on O3, with implications for the interpretation of policy-relevant metrics such as climate penalty.

Surface ozone (O3) pollution levels are strongly correlated with daytime surface temperatures, especially in highly polluted regions. This correlation is nonlinear and occurs through a variety of temperature dependent mechanisms related to O3 precursor emissions, lifetimes, and reaction rates, making the reproduction of temperature sensitivities – and the projection of associated human health risks – a complex problem. Here we explore the summertime O3–temperature relationship in the United States and Europe using the chemical transport model GEOS-Chem. We remove the temperature dependence of several mechanisms most frequently cited as causes of the O3–temperature climate penalty, including: PAN decomposition, soil NOx emissions, biogenic VOC emissions, and dry deposition. We quantify the contribution of each mechanism to the overall correlation between O3 and temperature both individually and collectively. Through this analysis we find that the thermal decomposition of PAN can explain, on average, 20 % of the overall O3–temperature correlation in the United States. The effect is weaker in Europe, explaining 9 % of the overall O3–temperature relationship. The temperature dependence of biogenic emissions contributes 3 % and 9 % of the total O3–temperature correlation in the United States and Europe on average, while temperature dependent deposition (6 % and 1 %) and soil NOx emissions (10 % and 7 %) also contribute. Even considered collectively these mechanisms explain less than 46 % of the modeled O3–temperature correlation in the United States and 36 % in Europe. We use commonality analysis to demonstrate that covariance with other meteorological phenomena such as stagnancy and humidity can explain the bulk of the remainder of the O3–temperature correlation. Thus, we demonstrate that the statistical correlation between O3 and temperature alone may greatly overestimate the direct impacts of temperature on O3, with implications for the interpretation of policy-relevant metrics such as climate penalty.

Anthropogenic land use change (LUC) since preindustrial (1850) has altered the vegetation distribution and density around the world. We use a global model (GEOS-Chem) to assess the attendant changes in surface air quality and the direct radiative forcing (DRF). We focus our analysis on secondary particulate matter and tropospheric ozone formation. The general trend of expansion of managed ecosystems (croplands and pasturelands) at the expense of natural ecosystems has led to an 11 % decline in global mean biogenic volatile organic compound emissions. Concomitant growth in agricultural activity has more than doubled ammonia emissions and increased emissions of nitrogen oxides from soils by more than 50 %. Conversion to croplands has also led to a widespread increase in ozone dry deposition velocity. Together these changes in biosphere–atmosphere exchange have led to a 14 % global mean increase in biogenic secondary organic aerosol (BSOA) surface concentrations, a doubling of surface aerosol nitrate concentrations, and local changes in surface ozone of up to 8.5 ppb. We assess a global mean LUC-DRF of +0.017, −0.071, and −0.01 W m−2 for BSOA, nitrate, and tropospheric ozone, respectively. We conclude that the DRF and the perturbations in surface air quality associated with LUC (and the associated changes in agricultural emissions) are substantial and should be considered alongside changes in anthropogenic emissions and climate feedbacks in chemistry–climate studies., National Science Foundation (U.S.) (ATM- 0929282 and ATM-1564495)