Accepted Manuscript:
A systematic re-evaluation of methods for quantification of bulk particle-phase organic nitrates using real-time aerosol mass spectrometry
Title: A systematic re-evaluation of methods for quantification of bulk particle-phase organic nitrates using real-time aerosol mass spectrometry
Abstract. Organic nitrate (RONO2) formation in the atmosphere represents a sink of NOx(NOx = NO + NO2) and termination of the NOx/HOx(HOx = HO2 + OH) ozone formation and radical propagation cycles, can act as a NOx reservoirtransporting reactive nitrogen, and contributes to secondary organic aerosol formation. While some fraction of RONO2 is thought to reside in the particle phase, particle-phase organic nitrates (pRONO2) are infrequently measured and thus poorly understood. There is anincreasing prevalence of aerosol mass spectrometer (AMS) instruments, which have shown promise for determining the quantitative total organic nitratefunctional group contribution to aerosols. A simple approach that relies on the relative intensities of NO+ and NO2+ ions inthe AMS spectrum, the calibrated NOx+ ratio for NH4NO3, and the inferred ratio for pRONO2 hasbeen proposed as a way to apportion the total nitrate signal to NH4NO3 and pRONO2. This method is increasingly beingapplied to field and laboratory data. However, the methods applied have been largely inconsistent and poorly characterized, and, therefore, adetailed evaluation is timely. Here, we compile an extensive survey of NOx+ ratios measured for variouspRONO2 compounds and mixtures from multiple AMS instruments, groups, and laboratory and field measurements. All data and analysispresented here are for use with the standard AMS vaporizer. We show that, more »
in the absence of pRONO2 standards, thepRONO2 NOx+ ratio can be estimated using a ratio referenced to the calibrated NH4NO3 ratio, aso-called “Ratio-of-Ratios” method (RoR = 2.75 ± 0.41). We systematically explore the basis for quantifyingpRONO2 (and NH4NO3) with the RoR method using ground and aircraft field measurements conducted over a largerange of conditions. The method is compared to another AMS method (positive matrix factorization, PMF) and other pRONO2 andrelated (e.g., total gas + particle RONO2) measurements, generally showing good agreement/correlation. A broad survey of ground andaircraft AMS measurements shows a pervasive trend of higher fractional contribution of pRONO2 to total nitrate with lower totalnitrate concentrations, which generally corresponds to shifts from urban-influenced to rural/remote regions. Compared to ground campaigns,observations from all aircraft campaigns showed substantially lower pRONO2 contributions at midranges of total nitrate(0.01–0.1 up to 2–5 µg m−3), suggesting that the balance of effects controlling NH4NO3 and pRONO2formation and lifetimes – such as higher humidity, lower temperatures, greater dilution, different sources, higher particle acidity, andpRONO2 hydrolysis (possibly accelerated by particle acidity) – favors lower pRONO2 contributions for thoseenvironments and altitudes sampled. « less
Abstract. Recent studies have revealed a significant influx of anthropogenic aerosol from South Asia to the Himalayas and Tibetan Plateau (TP) during pre-monsoon period. In order to characterize the chemical composition, sources, and transport processes of aerosol in this area, we carried out a field study during June 2015 by deploying a suite of online instruments including an Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-AMS) and a multi-angle absorption photometer (MAAP) at Nam Co station (90°57′E, 30°46′N; 4730ma.s.l.) at the central of the TP. The measurements were made at a period when the transition from pre-monsoon to monsoon occurred. The average ambient mass concentration of submicron particulate matter (PM1) over the whole campaign was ∼ 2.0µgm−3, with organics accounting for 68%, followed by sulfate (15%), black carbon (8%), ammonium (7%), and nitrate (2%). Relatively higher aerosol mass concentration episodes were observed during the pre-monsoon period, whereas persistently low aerosol concentrations were observed during the monsoon period. However, the chemical composition of aerosol during the higher aerosol concentration episodes in the pre-monsoon season was on a case-by-case basis, depending on the prevailing meteorological conditions and air mass transport routes. Mostmore »of the chemical species exhibited significant diurnal variations with higher values occurring during afternoon and lower values during early morning, whereas nitrate peaked during early morning in association with higher relative humidity and lower air temperature. Organic aerosol (OA), with an oxygen-to-carbon ratio (O∕C) of 0.94, was more oxidized during the pre-monsoon period than during monsoon (average O∕C ratio of 0.72), and an average O∕C was 0.88 over the entire campaign period, suggesting overall highly oxygenated aerosol in the central TP. Positive matrix factorization of the high-resolution mass spectra of OA identified two oxygenated organic aerosol (OOA) factors: a less oxidized OOA (LO-OOA) and a more oxidized OOA (MO-OOA). The MO-OOA dominated during the pre-monsoon period, whereas LO-OOA dominated during monsoon. The sensitivity of air mass transport during pre-monsoon with synoptic process was also evaluated with a 3-D chemical transport model.
Griffin, Debora; McLinden, Chris A.; Dammers, Enrico; Adams, Cristen; Stockwell, Chelsea E.; Warneke, Carsten; Bourgeois, Ilann; Peischl, Jeff; Ryerson, Thomas B.; Zarzana, Kyle J.; et al(
, Atmospheric Measurement Techniques)
Abstract. Smoke from wildfires is a significant source of air pollution, which can adversely impact air quality and ecosystems downwind. With the recently increasing intensity and severity of wildfires, the threat to air quality is expected to increase. Satellite-derived biomass burning emissions can fill in gaps in the absence of aircraft or ground-based measurement campaigns and can help improve the online calculation of biomass burning emissions as well as the biomass burning emissions inventories that feed air quality models. This study focuses on satellite-derived NOx emissions using the high-spatial-resolution TROPOspheric Monitoring Instrument (TROPOMI) NO2 dataset. Advancements and improvements to the satellite-based determination of forest fire NOx emissions are discussed, including information on plume height and effects of aerosol scattering and absorption on the satellite-retrieved vertical column densities. Two common top-down emission estimation methods, (1) an exponentially modified Gaussian (EMG) and (2) a flux method, are applied to synthetic data to determine the accuracy and the sensitivity to different parameters, including wind fields, satellite sampling, noise, lifetime, and plume spread. These tests show that emissions can be accurately estimated from single TROPOMI overpasses.The effect of smoke aerosols on TROPOMI NO2 columns (via air mass factors, AMFs) is estimated, and these satellitemore »columns and emission estimates are compared to aircraft observations from four different aircraft campaigns measuring biomass burning plumes in 2018 and 2019 in North America. Our results indicate that applying an explicit aerosol correction to the TROPOMI NO2 columns improves the agreement with the aircraft observations (by about 10 %–25 %). The aircraft- and satellite-derived emissions are in good agreement within the uncertainties. Both top-down emissions methods work well; however, the EMG method seems to output more consistent results and has better agreement with the aircraft-derived emissions. Assuming a Gaussian plume shape for various biomass burning plumes, we estimate an average NOx e-folding time of 2 ±1 h from TROPOMI observations. Based on chemistry transport model simulations and aircraft observations, the net emissions of NOx are 1.3 to 1.5 times greater than the satellite-derived NO2 emissions. A correction factor of 1.3 to 1.5 should thus be used to infer net NOx emissions from the satellite retrievals of NO2.« less
Alexander, Becky; Sherwen, Tomás; Holmes, Christopher D.; Fisher, Jenny A.; Chen, Qianjie; Evans, Mat J.; Kasibhatla, Prasad(
, Atmospheric Chemistry and Physics)
Abstract. The formation of inorganic nitrate is the main sink for nitrogenoxides (NOx = NO + NO2). Due to the importance of NOx forthe formation of tropospheric oxidants such as the hydroxyl radical (OH) andozone, understanding the mechanisms and rates of nitrate formation isparamount for our ability to predict the atmospheric lifetimes of mostreduced trace gases in the atmosphere. The oxygen isotopic composition ofnitrate (Δ17O(nitrate)) is determined by the relativeimportance of NOx sinks and thus can provide an observationalconstraint for NOx chemistry. Until recently, the ability to utilizeΔ17O(nitrate) observations for this purpose was hindered by ourlack of knowledge about the oxygen isotopic composition of ozone (Δ17O(O3)). Recent and spatially widespread observations of Δ17O(O3) motivate an updated comparison of modeled andobserved Δ17O(nitrate) and a reassessment of modeled nitrateformation pathways. Model updates based on recent laboratory studies ofheterogeneous reactions render dinitrogen pentoxide (N2O5)hydrolysis as important as NO2 + OH (both 41 %) for globalinorganic nitrate production near the surface (below 1 km altitude). Allother nitrate production mechanisms individually represent less than 6 %of global nitrate production near the surface but can be dominant locally.Updated reaction rates for aerosol uptake of NO2 result in significantreduction of nitrate and nitrous acid (HONO) formed through this pathway inthe model and render NO2 hydrolysis amore »negligible pathway for nitrateformation globally. Although photolysis of aerosol nitrate may haveimplications for NOx, HONO, and oxidant abundances, it does notsignificantly impact the relative importance of nitrate formation pathways.Modeled Δ17O(nitrate) (28.6±4.5 ‰)compares well with the average of a global compilation of observations (27.6±5.0 ‰) when assuming Δ17O(O3) = 26 ‰, giving confidence in the model'srepresentation of the relative importance of ozone versus HOx (= OH + HO2 + RO2) in NOx cycling and nitrate formation on theglobal scale.« less
Franchin, Alessandro; Fibiger, Dorothy L.; Goldberger, Lexie; McDuffie, Erin E.; Moravek, Alexander; Womack, Caroline C.; Crosman, Erik T.; Docherty, Kenneth S.; Dube, William P.; Hoch, Sebastian W.; et al(
, Atmospheric Chemistry and Physics)
Abstract. Airborne and ground-based measurements of aerosol concentrations, chemicalcomposition, and gas-phase precursors were obtained in three valleys innorthern Utah (USA). The measurements were part of the Utah Winter FineParticulate Study (UWFPS) that took place in January–February 2017. Totalaerosol mass concentrations of PM1 were measured from a Twin Otteraircraft, with an aerosol mass spectrometer (AMS). PM1 concentrationsranged from less than 2µgm−3 during clean periods to over100µgm−3 during the most polluted episodes, consistent withPM2.5 total mass concentrations measured concurrently at groundsites. Across the entire region, increases in total aerosol mass above∼2µgm−3 were associated with increases in theammonium nitrate mass fraction, clearly indicating that the highest aerosolmass loadings in the region were predominantly attributable to an increase inammonium nitrate. The chemical composition was regionally homogenous fortotal aerosol mass concentrations above 17.5µgm−3, with 74±5% (average±standard deviation) ammonium nitrate, 18±3%organic material, 6±3% ammonium sulfate, and 2±2%ammonium chloride. Vertical profiles of aerosol mass and volume in the regionshowed variable concentrations with height in the polluted boundary layer.Higher average mass concentrations were observed within themore »first few hundredmeters above ground level in all three valleys during pollution episodes. Gas-phase measurements of nitric acid (HNO3) and ammonia (NH3) duringthe pollution episodes revealed that in the Cache and Utah valleys, partitioningof inorganic semi-volatiles to the aerosol phase was usually limited by theamount of gas-phase nitric acid, with NH3 being in excess. The inorganicspecies were compared with the ISORROPIA thermodynamic model. Total inorganicaerosol mass concentrations were calculated for various decreases in totalnitrate and total ammonium. For pollution episodes, our simulations of a50% decrease in total nitrate lead to a 46±3% decrease in totalPM1 mass. A simulated 50% decrease in total ammonium leads to a36±17%µgm−3 decrease in total PM1 mass, over the entirearea of the study. Despite some differences among locations, ourresults showed a higher sensitivity to decreasing nitric acid concentrationsand the importance of ammonia at the lowest total nitrate conditions. In theSalt Lake Valley, both HNO3 and NH3 concentrations controlledaerosol formation.
Hussain, Mir Zaman; Hamilton, Stephen; Robertson, G. Philip; Basso, Bruno(
)
Abstract
Excessive phosphorus (P) applications to croplands can contribute to eutrophication of surface waters through surface runoff and subsurface (leaching) losses. We analyzed leaching losses of total dissolved P (TDP) from no-till corn, hybrid poplar (Populus nigra X P. maximowiczii), switchgrass (Panicum virgatum), miscanthus (Miscanthus giganteus), native grasses, and restored prairie, all planted in 2008 on former cropland in Michigan, USA. All crops except corn (13 kg P ha−1 year−1) were grown without P fertilization. Biomass was harvested at the end of each growing season except for poplar. Soil water at 1.2 m depth was sampled weekly to biweekly for TDP determination during March–November 2009–2016 using tension lysimeters. Soil test P (0–25 cm depth) was measured every autumn. Soil water TDP concentrations were usually below levels where eutrophication of surface waters is frequently observed (> 0.02 mg L−1) but often higher than in deep groundwater or nearby streams and lakes. Rates of P leaching, estimated from measured concentrations and modeled drainage, did not differ statistically among cropping systems across years; 7-year cropping system means ranged from 0.035 to 0.072 kg P ha−1 year−1 with large interannual variation. Leached P was positively related to STP, which decreased over the 7 years in all systems. These results indicate that both P-fertilized and unfertilized cropping systems may
leach legacy P from past cropland management.
Methods
Experimental details The Biofuel Cropping System Experiment (BCSE) is located at the W.K. Kellogg Biological Station (KBS) (42.3956° N, 85.3749° W; elevation 288 m asl) in southwestern Michigan, USA. This site is a part of the Great Lakes Bioenergy Research Center (www.glbrc.org) and is a Long-term Ecological Research site (www.lter.kbs.msu.edu). Soils are mesic Typic Hapludalfs developed on glacial outwash54 with high sand content (76% in the upper 150 cm) intermixed with silt-rich loess in the upper 50 cm55. The water table lies approximately 12–14 m below the surface. The climate is humid temperate with a mean annual air temperature of 9.1 °C and annual precipitation of 1005 mm, 511 mm of which falls between May and September (1981–2010)56,57. The BCSE was established as a randomized complete block design in 2008 on preexisting farmland. Prior to BCSE establishment, the field was used for grain crop and alfalfa (Medicago sativa L.) production for several decades. Between 2003 and 2007, the field received a total of ~ 300 kg P ha−1 as manure, and the southern half, which contains one of four replicate plots, received an additional 206 kg P ha−1 as inorganic fertilizer. The experimental design consists of five randomized blocks each containing one replicate plot (28 by 40 m) of 10 cropping systems (treatments) (Supplementary Fig. S1; also see Sanford et al.58). Block 5 is not included in the present study. Details on experimental design and site history are provided in Robertson and Hamilton57 and Gelfand et al.59. Leaching of P is analyzed in six of the cropping systems: (i) continuous no-till corn, (ii) switchgrass, (iii) miscanthus, (iv) a mixture of five species of native grasses, (v) a restored native prairie containing 18 plant species (Supplementary Table S1), and (vi) hybrid poplar. Agronomic management Phenological cameras and field observations indicated that the perennial herbaceous crops emerged each year between mid-April and mid-May. Corn was planted each year in early May. Herbaceous crops were harvested at the end of each growing season with the timing depending on weather: between October and November for corn and between November and December for herbaceous perennial crops. Corn stover was harvested shortly after corn grain, leaving approximately 10 cm height of stubble above the ground. The poplar was harvested only once, as the culmination of a 6-year rotation, in the winter of 2013–2014. Leaf emergence and senescence based on daily phenological images indicated the beginning and end of the poplar growing season, respectively, in each year. Application of inorganic fertilizers to the different crops followed a management approach typical for the region (Table 1). Corn was fertilized with 13 kg P ha−1 year−1 as starter fertilizer (N-P-K of 19-17-0) at the time of planting and an additional 33 kg P ha−1 year−1 was added as superphosphate in spring 2015. Corn also received N fertilizer around the time of planting and in mid-June at typical rates for the region (Table 1). No P fertilizer was applied to the perennial grassland or poplar systems (Table 1). All perennial grasses (except restored prairie) were provided 56 kg N ha−1 year−1 of N fertilizer in early summer between 2010 and 2016; an additional 77 kg N ha−1 was applied to miscanthus in 2009. Poplar was fertilized once with 157 kg N ha−1 in 2010 after the canopy had closed. Sampling of subsurface soil water and soil for P determination Subsurface soil water samples were collected beneath the root zone (1.2 m depth) using samplers installed at approximately 20 cm into the unconsolidated sand of 2Bt2 and 2E/Bt horizons (soils at the site are described in Crum and Collins54). Soil water was collected from two kinds of samplers: Prenart samplers constructed of Teflon and silica (http://www.prenart.dk/soil-water-samplers/) in replicate blocks 1 and 2 and Eijkelkamp ceramic samplers (http://www.eijkelkamp.com) in blocks 3 and 4 (Supplementary Fig. S1). The samplers were installed in 2008 at an angle using a hydraulic corer, with the sampling tubes buried underground within the plots and the sampler located about 9 m from the plot edge. There were no consistent differences in TDP concentrations between the two sampler types. Beginning in the 2009 growing season, subsurface soil water was sampled at weekly to biweekly intervals during non-frozen periods (April–November) by applying 50 kPa of vacuum to each sampler for 24 h, during which the extracted water was collected in glass bottles. Samples were filtered using different filter types (all 0.45 µm pore size) depending on the volume of leachate collected: 33-mm dia. cellulose acetate membrane filters when volumes were less than 50 mL; and 47-mm dia. Supor 450 polyethersulfone membrane filters for larger volumes. Total dissolved phosphorus (TDP) in water samples was analyzed by persulfate digestion of filtered samples to convert all phosphorus forms to soluble reactive phosphorus, followed by colorimetric analysis by long-pathlength spectrophotometry (UV-1800 Shimadzu, Japan) using the molybdate blue method60, for which the method detection limit was ~ 0.005 mg P L−1. Between 2009 and 2016, soil samples (0–25 cm depth) were collected each autumn from all plots for determination of soil test P (STP) by the Bray-1 method61, using as an extractant a dilute hydrochloric acid and ammonium fluoride solution, as is recommended for neutral to slightly acidic soils. The measured STP concentration in mg P kg−1 was converted to kg P ha−1 based on soil sampling depth and soil bulk density (mean, 1.5 g cm−3). Sampling of water samples from lakes, streams and wells for P determination In addition to chemistry of soil and subsurface soil water in the BCSE, waters from lakes, streams, and residential water supply wells were also sampled during 2009–2016 for TDP analysis using Supor 450 membrane filters and the same analytical method as for soil water. These water bodies are within 15 km of the study site, within a landscape mosaic of row crops, grasslands, deciduous forest, and wetlands, with some residential development (Supplementary Fig. S2, Supplementary Table S2). Details of land use and cover change in the vicinity of KBS are given in Hamilton et al.48, and patterns in nutrient concentrations in local surface waters are further discussed in Hamilton62. Leaching estimates, modeled drainage, and data analysis Leaching was estimated at daily time steps and summarized as total leaching on a crop-year basis, defined from the date of planting or leaf emergence in a given year to the day prior to planting or emergence in the following year. TDP concentrations (mg L−1) of subsurface soil water were linearly interpolated between sampling dates during non-freezing periods (April–November) and over non-sampling periods (December–March) based on the preceding November and subsequent April samples. Daily rates of TDP leaching (kg ha−1) were calculated by multiplying concentration (mg L−1) by drainage rates (m3 ha−1 day−1) modeled by the Systems Approach for Land Use Sustainability (SALUS) model, a crop growth model that is well calibrated for KBS soil and environmental conditions. SALUS simulates yield and environmental outcomes in response to weather, soil, management (planting dates, plant population, irrigation, N fertilizer application, and tillage), and genetics63. The SALUS water balance sub-model simulates surface runoff, saturated and unsaturated water flow, drainage, root water uptake, and evapotranspiration during growing and non-growing seasons63. The SALUS model has been used in studies of evapotranspiration48,51,64 and nutrient leaching20,65,66,67 from KBS soils, and its predictions of growing-season evapotranspiration are consistent with independent measurements based on growing-season soil water drawdown53 and evapotranspiration measured by eddy covariance68. Phosphorus leaching was assumed insignificant on days when SALUS predicted no drainage. Volume-weighted mean TDP concentrations in leachate for each crop-year and for the entire 7-year study period were calculated as the total dissolved P leaching flux (kg ha−1) divided by the total drainage (m3 ha−1). One-way ANOVA with time (crop-year) as the fixed factor was conducted to compare total annual drainage rates, P leaching rates, volume-weighted mean TDP concentrations, and maximum aboveground biomass among the cropping systems over all seven crop-years as well as with TDP concentrations from local lakes, streams, and groundwater wells. When a significant (α = 0.05) difference was detected among the groups, we used the Tukey honest significant difference (HSD) post-hoc test to make pairwise comparisons among the groups. In the case of maximum aboveground biomass, we used the Tukey–Kramer method to make pairwise comparisons among the groups because the absence of poplar data after the 2013 harvest resulted in unequal sample sizes. We also used the Tukey–Kramer method to compare the frequency distributions of TDP concentrations in all of the soil leachate samples with concentrations in lakes, streams, and groundwater wells, since each sample category had very different numbers of measurements.
Other
Individual spreadsheets in “data table_leaching_dissolved organic carbon and nitrogen.xls” 1. annual precip_drainage 2. biomass_corn, perennial grasses 3. biomass_poplar 4. annual N leaching _vol-wtd conc 5. Summary_N leached 6. annual DOC leachin_vol-wtd conc 7. growing season length 8. correlation_nh4 VS no3 9. correlations_don VS no3_doc VS don Each spreadsheet is described below along with an explanation of variates. Note that ‘nan’ indicate data are missing or not available. First row indicates header; second row indicates units 1. Spreadsheet: annual precip_drainage Description: Precipitation measured from nearby Kellogg Biological Station (KBS) Long Term Ecological Research (LTER) Weather station, over 2009-2016 study period. Data shown in Figure 1; original data source for precipitation (https://lter.kbs.msu.edu/datatables/7). Drainage estimated from SALUS crop model. Note that drainage is percolation out of the root zone (0-125 cm). Annual precipitation and drainage values shown here are calculated for growing and non-growing crop periods. Variate Description year year of the observation crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” precip_G precipitation during growing period (milliMeter) precip_NG precipitation during non-growing period (milliMeter) drainage_G drainage during growing period (milliMeter) drainage_NG drainage during non-growing period (milliMeter) 2. Spreadsheet: biomass_corn, perennial grasses Description: Maximum aboveground biomass measurements from corn, switchgrass, miscanthus, native grass and restored prairie plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Data shown in Figure 2. Variate Description year year of the observation date day of the observation (mm/dd/yyyy) crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” replicate each crop has four replicated plots, R1, R2, R3 and R4 station stations (S1, S2 and S3) of samplings within the plot. For more details, refer to link (https://data.sustainability.glbrc.org/protocols/156) species plant species that are rooted within the quadrat during the time of maximum biomass harvest. See protocol for more information, refer to link (http://lter.kbs.msu.edu/datatables/36) For maize biomass, grain and whole biomass reported in the paper (weed biomass or surface litter are excluded). Surface litter biomass not included in any crops; weed biomass not included in switchgrass and miscanthus, but included in grass mixture and prairie. fraction Fraction of biomass biomass_plot biomass per plot on dry-weight basis (Grams_Per_SquareMeter) biomass_ha biomass (megaGrams_Per_Hectare) by multiplying column biomass per plot with 0.01 3. Spreadsheet: biomass_poplar Description: Maximum aboveground biomass measurements from poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Data shown in Figure 2. Note that poplar biomass was estimated from crop growth curves until the poplar was harvested in the winter of 2013-14. Variate Description year year of the observation method methods of poplar biomass sampling date day of the observation (mm/dd/yyyy) replicate each crop has four replicated plots, R1, R2, R3 and R4 diameter_at_ground poplar diameter (milliMeter) at the ground diameter_at_15cm poplar diameter (milliMeter) at 15 cm height biomass_tree biomass per plot (Grams_Per_Tree) biomass_ha biomass (megaGrams_Per_Hectare) by multiplying biomass per tree with 0.01 4. Spreadsheet: annual N leaching_vol-wtd conc Description: Annual leaching rate (kiloGrams_N_Per_Hectare) and volume-weighted mean N concentrations (milliGrams_N_Per_Liter) of nitrate (no3) and dissolved organic nitrogen (don) in the leachate samples collected from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for nitrogen leached and volume-wtd mean N concentration shown in Figure 3a and Figure 3b, respectively. Note that ammonium (nh4) concentration were much lower and often undetectable (<0.07 milliGrams_N_Per_Liter). Also note that in 2009 and 2010 crop-years, data from some replicates are missing. Variate Description crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” crop-year year of the observation replicate each crop has four replicated plots, R1, R2, R3 and R4 no3 leached annual leaching rates of nitrate (kiloGrams_N_Per_Hectare) don leached annual leaching rates of don (kiloGrams_N_Per_Hectare) vol-wtd no3 conc. Volume-weighted mean no3 concentration (milliGrams_N_Per_Liter) vol-wtd don conc. Volume-weighted mean don concentration (milliGrams_N_Per_Liter) 5. Spreadsheet: summary_N leached Description: Summary of total amount and forms of N leached (kiloGrams_N_Per_Hectare) and the percent of applied N lost to leaching over the seven years for corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for nitrogen amount leached shown in Figure 4a and percent of applied N lost shown in Figure 4b. Note the fraction of unleached N includes in harvest, accumulation in root biomass, soil organic matter or gaseous N emissions were not measured in the study. Variate Description crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” no3 leached annual leaching rates of nitrate (kiloGrams_N_Per_Hectare) don leached annual leaching rates of don (kiloGrams_N_Per_Hectare) N unleached N unleached (kiloGrams_N_Per_Hectare) in other sources are not studied % of N applied N lost to leaching % of N applied N lost to leaching 6. Spreadsheet: annual DOC leachin_vol-wtd conc Description: Annual leaching rate (kiloGrams_Per_Hectare) and volume-weighted mean N concentrations (milliGrams_Per_Liter) of dissolved organic carbon (DOC) in the leachate samples collected from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for DOC leached and volume-wtd mean DOC concentration shown in Figure 5a and Figure 5b, respectively. Note that in 2009 and 2010 crop-years, water samples were not available for DOC measurements. Variate Description crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” crop-year year of the observation replicate each crop has four replicated plots, R1, R2, R3 and R4 doc leached annual leaching rates of nitrate (kiloGrams_Per_Hectare) vol-wtd doc conc. volume-weighted mean doc concentration (milliGrams_Per_Liter) 7. Spreadsheet: growing season length Description: Growing season length (days) of corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in the Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Date shown in Figure S2. Note that growing season is from the date of planting or emergence to the date of harvest (or leaf senescence in case of poplar). Variate Description crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” year year of the observation growing season length growing season length (days) 8. Spreadsheet: correlation_nh4 VS no3 Description: Correlation of ammonium (nh4+) and nitrate (no3-) concentrations (milliGrams_N_Per_Liter) in the leachate samples from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2013-2015. Data shown in Figure S3. Note that nh4+ concentration in the leachates was very low compared to no3- and don concentration and often undetectable in three crop-years (2013-2015) when measurements are available. Variate Description crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” date date of the observation (mm/dd/yyyy) replicate each crop has four replicated plots, R1, R2, R3 and R4 nh4 conc nh4 concentration (milliGrams_N_Per_Liter) no3 conc no3 concentration (milliGrams_N_Per_Liter) 9. Spreadsheet: correlations_don VS no3_doc VS don Description: Correlations of don and nitrate concentrations (milliGrams_N_Per_Liter); and doc (milliGrams_Per_Liter) and don concentrations (milliGrams_N_Per_Liter) in the leachate samples of corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2013-2015. Data of correlation of don and nitrate concentrations shown in Figure S4 a and doc and don concentrations shown in Figure S4 b. Variate Description crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” year year of the observation don don concentration (milliGrams_N_Per_Liter) no3 no3 concentration (milliGrams_N_Per_Liter) doc doc concentration (milliGrams_Per_Liter) More>>
Day, Douglas A., Campuzano-Jost, Pedro, Nault, Benjamin A., Palm, Brett B., Hu, Weiwei, Guo, Hongyu, Wooldridge, Paul J., Cohen, Ronald C., Docherty, Kenneth S., Huffman, J. Alex, de Sá, Suzane S., Martin, Scot T., and Jimenez, Jose L.. A systematic re-evaluation of methods for quantification of bulk particle-phase organic nitrates using real-time aerosol mass spectrometry. Retrieved from https://par.nsf.gov/biblio/10386149. Atmospheric Measurement Techniques 15.2 Web. doi:10.5194/amt-15-459-2022.
Day, Douglas A., Campuzano-Jost, Pedro, Nault, Benjamin A., Palm, Brett B., Hu, Weiwei, Guo, Hongyu, Wooldridge, Paul J., Cohen, Ronald C., Docherty, Kenneth S., Huffman, J. Alex, de Sá, Suzane S., Martin, Scot T., & Jimenez, Jose L.. A systematic re-evaluation of methods for quantification of bulk particle-phase organic nitrates using real-time aerosol mass spectrometry. Atmospheric Measurement Techniques, 15 (2). Retrieved from https://par.nsf.gov/biblio/10386149. https://doi.org/10.5194/amt-15-459-2022
Day, Douglas A., Campuzano-Jost, Pedro, Nault, Benjamin A., Palm, Brett B., Hu, Weiwei, Guo, Hongyu, Wooldridge, Paul J., Cohen, Ronald C., Docherty, Kenneth S., Huffman, J. Alex, de Sá, Suzane S., Martin, Scot T., and Jimenez, Jose L..
"A systematic re-evaluation of methods for quantification of bulk particle-phase organic nitrates using real-time aerosol mass spectrometry". Atmospheric Measurement Techniques 15 (2). Country unknown/Code not available. https://doi.org/10.5194/amt-15-459-2022.https://par.nsf.gov/biblio/10386149.
@article{osti_10386149,
place = {Country unknown/Code not available},
title = {A systematic re-evaluation of methods for quantification of bulk particle-phase organic nitrates using real-time aerosol mass spectrometry},
url = {https://par.nsf.gov/biblio/10386149},
DOI = {10.5194/amt-15-459-2022},
abstractNote = {Abstract. Organic nitrate (RONO2) formation in the atmosphere represents a sink of NOx(NOx = NO + NO2) and termination of the NOx/HOx(HOx = HO2 + OH) ozone formation and radical propagation cycles, can act as a NOx reservoirtransporting reactive nitrogen, and contributes to secondary organic aerosol formation. While some fraction of RONO2 is thought to reside in the particle phase, particle-phase organic nitrates (pRONO2) are infrequently measured and thus poorly understood. There is anincreasing prevalence of aerosol mass spectrometer (AMS) instruments, which have shown promise for determining the quantitative total organic nitratefunctional group contribution to aerosols. A simple approach that relies on the relative intensities of NO+ and NO2+ ions inthe AMS spectrum, the calibrated NOx+ ratio for NH4NO3, and the inferred ratio for pRONO2 hasbeen proposed as a way to apportion the total nitrate signal to NH4NO3 and pRONO2. This method is increasingly beingapplied to field and laboratory data. However, the methods applied have been largely inconsistent and poorly characterized, and, therefore, adetailed evaluation is timely. Here, we compile an extensive survey of NOx+ ratios measured for variouspRONO2 compounds and mixtures from multiple AMS instruments, groups, and laboratory and field measurements. All data and analysispresented here are for use with the standard AMS vaporizer. We show that, in the absence of pRONO2 standards, thepRONO2 NOx+ ratio can be estimated using a ratio referenced to the calibrated NH4NO3 ratio, aso-called “Ratio-of-Ratios” method (RoR = 2.75 ± 0.41). We systematically explore the basis for quantifyingpRONO2 (and NH4NO3) with the RoR method using ground and aircraft field measurements conducted over a largerange of conditions. The method is compared to another AMS method (positive matrix factorization, PMF) and other pRONO2 andrelated (e.g., total gas + particle RONO2) measurements, generally showing good agreement/correlation. A broad survey of ground andaircraft AMS measurements shows a pervasive trend of higher fractional contribution of pRONO2 to total nitrate with lower totalnitrate concentrations, which generally corresponds to shifts from urban-influenced to rural/remote regions. Compared to ground campaigns,observations from all aircraft campaigns showed substantially lower pRONO2 contributions at midranges of total nitrate(0.01–0.1 up to 2–5 µg m−3), suggesting that the balance of effects controlling NH4NO3 and pRONO2formation and lifetimes – such as higher humidity, lower temperatures, greater dilution, different sources, higher particle acidity, andpRONO2 hydrolysis (possibly accelerated by particle acidity) – favors lower pRONO2 contributions for thoseenvironments and altitudes sampled.},
journal = {Atmospheric Measurement Techniques},
volume = {15},
number = {2},
author = {Day, Douglas A. and Campuzano-Jost, Pedro and Nault, Benjamin A. and Palm, Brett B. and Hu, Weiwei and Guo, Hongyu and Wooldridge, Paul J. and Cohen, Ronald C. and Docherty, Kenneth S. and Huffman, J. Alex and de Sá, Suzane S. and Martin, Scot T. and Jimenez, Jose L.},
}