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1. Oceanic emissions of dimethyl sulfide and methanethiol and their contribution to sulfur dioxide production in the marine atmosphere

   https://doi.org/10.5194/acp-22-6309-2022  

   Novak, Gordon A. ; Kilgour, Delaney B. ; Jernigan, Christopher M. ; Vermeuel, Michael P. ; Bertram, Timothy H. ( January 2022 , Atmospheric Chemistry and Physics)

   Abstract. Oceanic emissions of dimethyl sulfide (CH3SCH3,DMS) have long been recognized to impact aerosol particle composition andsize, the concentration of cloud condensation nuclei (CCN), and Earth'sradiation balance. The impact of oceanic emissions of methanethiol(CH3SH, MeSH), which is produced by the same oceanic precursor as DMS,on the volatile sulfur budget of the marine atmosphere is largelyunconstrained. Here we present direct flux measurements of MeSH oceanicemissions using the eddy covariance (EC) method with a high-resolutionproton-transfer-reaction time-of-flight mass spectrometer (PTR-ToFMS)detector and compare them to simultaneous flux measurements of DMS emissionsfrom a coastal ocean site. Campaign mean mixing ratios of DMS and MeSH were72 ppt (28–90 ppt interquartile range) and 19.1 ppt (7.6–24.5 pptinterquartile range), respectively. Campaign mean emission fluxes of DMS (FDMS) and MeSH (FMeSH) were 1.13 ppt m s−1 (0.53–1.61 ppt m s−1 interquartile range) and 0.21 ppt m s−1 (0.10–0.31 ppt m s−1 interquartile range), respectively. Linear least squares regression of observed MeSH and DMS flux indicates the emissions are highly correlatedwith each other (R2=0.65) over the course of the campaign,consistent with a shared oceanic source. The campaign mean DMS to MeSH fluxratio (FDMS:FMeSH) was 5.5 ± 3.0, calculated from the ratio of 304 individual coincident measurements of FDMS and FMeSH. Measured FDMS:FMeSH was weakly correlated (R2=0.15) withocean chlorophyll concentrations, with FDMS:FMeSH reaching a maximumof 10.8 ± 4.4 during a phytoplankton bloom period. No other volatilesulfur compounds were observed by PTR-ToFMS to have a resolvable emissionflux above their flux limit of detection or to have a gas-phase mixing ratio consistently above their limit of detection during the study period,suggesting DMS and MeSH are the dominant volatile organic sulfur compoundsemitted from the ocean at this site. The impact of this MeSH emission source on atmospheric budgets of sulfurdioxide (SO2) was evaluated by implementing observed emissions in a coupled ocean–atmosphere chemical box model using a newly compiled MeSHoxidation mechanism. Model results suggest that MeSH emissions lead toafternoon instantaneous SO2 production of 2.5 ppt h−1, which results in a 43 % increase in total SO2 production compared to a casewhere only DMS emissions are considered and accounts for 30% of theinstantaneous SO2 production in the marine boundary layer at the meanmeasured FDMS and FMeSH. This contribution of MeSH to SO2production is driven by a higher effective yield of SO2 from MeSHoxidation and the shorter oxidation lifetime of MeSH compared to DMS. Thislarge additional source of marine SO2 has not been previouslyconsidered in global models of marine sulfur cycling. The field measurementsand modeling results presented here demonstrate that MeSH is an importantcontributor to volatile sulfur budgets in the marine atmosphere and must be measured along with DMS in order to constrain marine sulfur budgets. Thislarge additional source of marine–reduced sulfur from MeSH will contribute to particle formation and growth and CCN abundance in the marine atmosphere, with subsequent impacts on climate. 

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2. OH and HO₂ radical chemistry in a midlatitude forest: measurements and model comparisons

   https://doi.org/10.5194/acp-20-9209-2020  

   Lew, Michelle M. ; Rickly, Pamela S. ; Bottorff, Brandon P. ; Reidy, Emily ; Sklaveniti, Sofia ; Léonardis, Thierry ; Locoge, Nadine ; Dusanter, Sebastien ; Kundu, Shuvashish ; Wood, Ezra ; et al ( January 2020 , Atmospheric Chemistry and Physics)

   null (Ed.)

   Abstract. Reactions of the hydroxyl (OH) and peroxy (HO2 and RO2) radicals playa central role in the chemistry of the atmosphere. In addition to controlling the lifetimes ofmany trace gases important to issues of global climate change, OH radical reactionsinitiate the oxidation of volatile organic compounds (VOCs) which can lead to the production ofozone and secondary organic aerosols in the atmosphere. Previous measurements of these radicalsin forest environments characterized by high mixing ratios of isoprene and low mixing ratios ofnitrogen oxides (NOx) (typically less than 1–2 ppb) have shown seriousdiscrepancies with modeled concentrations. These results bring into question our understanding ofthe atmospheric chemistry of isoprene and other biogenic VOCs under low NOxconditions. During the summer of 2015, OH and HO2 radical concentrations, as well as totalOH reactivity, were measured using laser-induced fluorescence–fluorescence assay by gasexpansion (LIF-FAGE) techniques as part of the Indiana Radical Reactivity and Ozone productioN InterComparison (IRRONIC). This campaign took place in a forested area near Indiana University's Bloomington campus which is characterized by high mixing ratios of isoprene (average daily maximum ofapproximately 4 ppb at 28 ∘C) and low mixing ratios of NO (diurnal averageof approximately 170 ppt). Supporting measurements of photolysis rates, VOCs,NOx, and other species were used to constrain a zero-dimensional box model basedon the Regional Atmospheric Chemistry Mechanism (RACM2) and the Master Chemical Mechanism (MCM 3.2),including versions of the Leuven isoprene mechanism (LIM1) for HOx regeneration(RACM2-LIM1 and MCM 3.3.1). Using an OH chemical scavenger technique, the study revealed thepresence of an interference with the LIF-FAGE measurements of OH that increased with bothambient concentrations of ozone and temperature with an average daytime maximum equivalentOH concentration of approximately 5×106 cm−3. Subtraction of theinterference resulted in measured OH concentrations of approximately4×106 cm−3 (average daytime maximum) that were in better agreement with modelpredictions although the models underestimated the measurements in the evening. The addition ofversions of the LIM1 mechanism increased the base RACM2 and MCM 3.2 modeled OH concentrationsby approximately 20 % and 13 %, respectively, with the RACM2-LIM1 mechanism providing thebest agreement with the measured concentrations, predicting maximum daily OH concentrationsto within 30 % of the measured concentrations. Measurements of HO2 concentrationsduring the campaign (approximately a 1×109 cm−3 average daytime maximum)included a fraction of isoprene-based peroxy radicals(HO2*=HO2+αRO2) and were found to agree with modelpredictions to within 10 %–30 %. On average, the measured reactivity was consistent with thatcalculated from measured OH sinks to within 20 %, with modeled oxidation productsaccounting for the missing reactivity, however significant missing reactivity (approximately40 % of the total measured reactivity) was observed on some days. 

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3. Surface–atmosphere fluxes of volatile organic compounds in Beijing

   https://doi.org/10.5194/acp-20-15101-2020  

   Acton, W. Joe ; Huang, Zhonghui ; Davison, Brian ; Drysdale, Will S. ; Fu, Pingqing ; Hollaway, Michael ; Langford, Ben ; Lee, James ; Liu, Yanhui ; Metzger, Stefan ; et al ( January 2020 , Atmospheric Chemistry and Physics)

   null (Ed.)

   Abstract. Mixing ratios of volatile organic compounds (VOCs) were recordedin two field campaigns in central Beijing as part of the Air Pollution andHuman Health in a Chinese Megacity (APHH) project. These data were used tocalculate, for the first time in Beijing, the surface–atmosphere fluxes ofVOCs using eddy covariance, giving a top-down estimation of VOC emissionsfrom a central area of the city. The results were then used to evaluate theaccuracy of the Multi-resolution Emission Inventory for China (MEIC). TheAPHH winter and summer campaigns took place in November and December 2016and May and June 2017, respectively. The largest VOC fluxes observed were ofsmall oxygenated compounds such as methanol, ethanol + formic acid andacetaldehyde, with average emission rates of 8.31 ± 8.5, 3.97 ± 3.9 and 1.83 ± 2.0 nmol m−2 s−1, respectively, in the summer.A large flux of isoprene was observed in the summer, with an average emissionrate of 5.31 ± 7.7 nmol m−2 s−1. While oxygenated VOCs madeup 60 % of the molar VOC flux measured, when fluxes were scaled by ozoneformation potential and peroxyacyl nitrate (PAN) formation potential thehigh reactivity of isoprene and monoterpenes meant that these speciesrepresented 30 % and 28 % of the flux contribution to ozone and PANformation potential, respectively. Comparison of measured fluxes with theemission inventory showed that the inventory failed to capture the magnitudeof VOC emissions at the local scale. 

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4. Phosphorus availability and leaching losses in annual and perennial cropping systems in an upper US Midwest landscape

   https://doi.org/10.5061/dryad.8sf7m0cpx  

   Hussain, Mir Zaman ; Hamilton, Stephen ; Robertson, G. Philip ; Basso, Bruno ( January 2021 , Dryad)

   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. 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. 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) 

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5. Observations of biogenic volatile organic compounds over a mixed temperate forest during the summer to autumn transition

   https://doi.org/10.5194/acp-23-4123-2023  

   Vermeuel, Michael P. ; Novak, Gordon A. ; Kilgour, Delaney B. ; Claflin, Megan S. ; Lerner, Brian M. ; Trowbridge, Amy M. ; Thom, Jonathan ; Cleary, Patricia A. ; Desai, Ankur R. ; Bertram, Timothy H. ( January 2023 , Atmospheric Chemistry and Physics)

   Abstract. The exchange of trace gases between the biosphere and the atmosphere is an important process that controls both chemical and physical properties of the atmosphere with implications for air quality and climate change. The terrestrial biosphere is a major source of reactive biogenic volatile organic compounds (BVOCs) that govern atmospheric concentrations of the hydroxy radical (OH) and ozone (O3) and control the formation andgrowth of secondary organic aerosol (SOA). Common simulations of BVOCsurface–atmosphere exchange in chemical transport models use parameterizations derived from the growing season and do not considerpotential changes in emissions during seasonal transitions. Here, we useobservations of BVOCs over a mixed temperate forest in northern Wisconsinduring broadleaf senescence to better understand the effects of the seasonal changes in canopy conditions (e.g., temperature, sunlight, leaf area, and leaf stage) on net BVOC exchange. The BVOCs investigated here include the terpenoids isoprene (C5H8), monoterpenes (MTs; C10H16), a monoterpene oxide (C10H16O), and sesquiterpenes (SQTs; C15H24), as well as a subset of other monoterpene oxides and dimethyl sulfide (DMS). During this period, MTs were primarily composed of α-pinene, β-pinene, and camphene, with α-pinene and camphene dominant during the first half of September and β-pinene thereafter. We observed enhanced MT and monoterpene oxide emissions following the onset of leaf senescence and suggest that senescence has the potential to be a significant control on late-season MT emissions in this ecosystem. We show that common parameterizations of BVOC emissions cannot reproduce the fluxes of MT, C10H16O, and SQT during the onset and continuation of senescence but can correctly simulate isoprene flux. We also describe the impact of the MT emission enhancement on the potential to form highly oxygenated organic molecules (HOMs). The calculated production rates of HOMs and H2SO4, constrained by terpene and DMS concentrations, suggest that biogenic aerosol formation and growth in this region should be dominated by secondary organics rather than sulfate. Further, we show that models using parameterized MT emissions likely underestimate HOM production, and thus aerosol growth and formation, during early autumn in this region. Further measurements of forest–atmosphere BVOC exchange during seasonal transitions as well as measurements of DMS in temperate regions are needed to effectively predict the effects of canopy changes on reactive carbon cycling and aerosol production. 

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