Pub Date : 2022-01-01DOI: 10.1525/elementa.2022.00023
Lukas T. Bernhardt, Richard G. Smith, A. S. Grandy, J. Mackay, Nicholas D. Warren, K. Geyer, J. Ernakovich
The physicochemical environment within aggregates controls the distribution of carbon and microbial communities in soils. Agricultural management, such as tillage, can disrupt aggregates and the microscale habitat provided to microorganisms, thus altering microbial community dynamics. Categorizing microbial communities into life history strategies with shared functional traits—as has been done to understand plant community structure for decades—can illuminate how the soil physicochemical environment constrains the membership and activity of microbial communities. We conducted an aggregate scale survey of microbial community composition and function through the lens of the yield–acquisition–stress (Y–A–S) tolerator life history framework. Soils collected from a 7-year tillage experiment were separated into 4 aggregate size classes and enzyme activity, multiple-substrate-induced respiration, and carbon use efficiency were measured to reveal trade-offs in microbial resource allocation. Microbial community structure was interrogated with bacterial and fungal marker gene sequencing, and metagenomic features such as community weighted genome size and traits conferring stress tolerance were predicted using PICRUSt2. Consistent with our hypothesis, aggregates of different size classes harbored distinct microbial communities manifesting distinct life history strategies. Large macroaggregate communities >2 mm were classified as acquisition strategists based on increased enzyme activity relative to other aggregate size classes. Small and medium microaggregate (0.25–2 mm) communities did not show a strong tendency toward any particular life history strategy. Genes conferring stress tolerance were significantly enriched in microaggregates <0.25 mm (indicative of stress tolerators); however, these communities also had the highest carbon use efficiency (indicative of yield strategists). We found trade-offs in resource allocation between communities classified as yield and acquisition strategists consistent with the Y–A–S framework. Tillage did not alter life history strategies within aggregates, suggesting that the aggregate physicochemistry plays a larger role than agricultural management in shaping microbial life history at the scale studied.
{"title":"Soil microbial communities vary in composition and functional strategy across soil aggregate size class regardless of tillage","authors":"Lukas T. Bernhardt, Richard G. Smith, A. S. Grandy, J. Mackay, Nicholas D. Warren, K. Geyer, J. Ernakovich","doi":"10.1525/elementa.2022.00023","DOIUrl":"https://doi.org/10.1525/elementa.2022.00023","url":null,"abstract":"The physicochemical environment within aggregates controls the distribution of carbon and microbial communities in soils. Agricultural management, such as tillage, can disrupt aggregates and the microscale habitat provided to microorganisms, thus altering microbial community dynamics. Categorizing microbial communities into life history strategies with shared functional traits—as has been done to understand plant community structure for decades—can illuminate how the soil physicochemical environment constrains the membership and activity of microbial communities. We conducted an aggregate scale survey of microbial community composition and function through the lens of the yield–acquisition–stress (Y–A–S) tolerator life history framework. Soils collected from a 7-year tillage experiment were separated into 4 aggregate size classes and enzyme activity, multiple-substrate-induced respiration, and carbon use efficiency were measured to reveal trade-offs in microbial resource allocation. Microbial community structure was interrogated with bacterial and fungal marker gene sequencing, and metagenomic features such as community weighted genome size and traits conferring stress tolerance were predicted using PICRUSt2. Consistent with our hypothesis, aggregates of different size classes harbored distinct microbial communities manifesting distinct life history strategies. Large macroaggregate communities >2 mm were classified as acquisition strategists based on increased enzyme activity relative to other aggregate size classes. Small and medium microaggregate (0.25–2 mm) communities did not show a strong tendency toward any particular life history strategy. Genes conferring stress tolerance were significantly enriched in microaggregates <0.25 mm (indicative of stress tolerators); however, these communities also had the highest carbon use efficiency (indicative of yield strategists). We found trade-offs in resource allocation between communities classified as yield and acquisition strategists consistent with the Y–A–S framework. Tillage did not alter life history strategies within aggregates, suggesting that the aggregate physicochemistry plays a larger role than agricultural management in shaping microbial life history at the scale studied.","PeriodicalId":54279,"journal":{"name":"Elementa-Science of the Anthropocene","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66943365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1525/elementa.2022.00024
Vanessa Engelhardt, T. Perez, L. Donoso, T. Müller, A. Wiedensohler
Atmospheric aerosols play an important role in atmospheric processes and human health. Characterizing atmospheric aerosols and identifying their sources in large cities is relevant to propose site-specific air pollution mitigation strategies. In this study, we measured the mass concentration of atmospheric aerosols with an aerodynamic diameter smaller than 2.5 µm (PM2.5) in the city of Caracas (urban) and in a tropical montane cloud forest (suburban site, located in a mountainous area 11 km far from Caracas) between June 2018 and October 2019. We also measured equivalent black carbon (eBC) mass concentration in PM2.5 in Caracas during the same period. Our goal is to assess PM2.5 and eBC temporal variation and identify their major sources in the area. eBC showed a pronounced diurnal cycle in the urban site, mainly modulated by traffic circulation and the diurnal changes of the mixing layer height. In contrast, PM2.5 showed stable median values during the day with slight variations like that of eBC. In the forest site, PM2.5 values were higher in the afternoons due to the convective transport of aerosols from Caracas and other surrounding urban areas located in adjacent valleys. The annual median for eBC and PM2.5 was 1.6 and 9.2 µg m–3, respectively, in the urban site, while PM2.5 in the forest site was 6.6 µg m–3. To our knowledge, these are the first measurements of this type in the northernmost area of South America. eBC and PM2.5 sources identification during wet and dry seasons was obtained by percentiles of the conditional bivariate probability function (CBPF). CBPF showed seasonal variations of eBC and PM2.5 sources and that their contributions are higher during the dry season. Biomass burning events are a relevant contributing source of aerosols for both sites of measurements inferred by fire pixels from satellite data, the national fire department’s statistics data, and backward trajectories. Our results indicate that biomass burning might affect the atmosphere on a regional scale, contribute to regional warming, and have implications for local and regional air quality and, therefore, human health.
{"title":"Black carbon and particulate matter mass concentrations in the Metropolitan District of Caracas, Venezuela: An assessment of temporal variation and contributing sources","authors":"Vanessa Engelhardt, T. Perez, L. Donoso, T. Müller, A. Wiedensohler","doi":"10.1525/elementa.2022.00024","DOIUrl":"https://doi.org/10.1525/elementa.2022.00024","url":null,"abstract":"Atmospheric aerosols play an important role in atmospheric processes and human health. Characterizing atmospheric aerosols and identifying their sources in large cities is relevant to propose site-specific air pollution mitigation strategies. In this study, we measured the mass concentration of atmospheric aerosols with an aerodynamic diameter smaller than 2.5 µm (PM2.5) in the city of Caracas (urban) and in a tropical montane cloud forest (suburban site, located in a mountainous area 11 km far from Caracas) between June 2018 and October 2019. We also measured equivalent black carbon (eBC) mass concentration in PM2.5 in Caracas during the same period. Our goal is to assess PM2.5 and eBC temporal variation and identify their major sources in the area. eBC showed a pronounced diurnal cycle in the urban site, mainly modulated by traffic circulation and the diurnal changes of the mixing layer height. In contrast, PM2.5 showed stable median values during the day with slight variations like that of eBC. In the forest site, PM2.5 values were higher in the afternoons due to the convective transport of aerosols from Caracas and other surrounding urban areas located in adjacent valleys. The annual median for eBC and PM2.5 was 1.6 and 9.2 µg m–3, respectively, in the urban site, while PM2.5 in the forest site was 6.6 µg m–3. To our knowledge, these are the first measurements of this type in the northernmost area of South America. eBC and PM2.5 sources identification during wet and dry seasons was obtained by percentiles of the conditional bivariate probability function (CBPF). CBPF showed seasonal variations of eBC and PM2.5 sources and that their contributions are higher during the dry season. Biomass burning events are a relevant contributing source of aerosols for both sites of measurements inferred by fire pixels from satellite data, the national fire department’s statistics data, and backward trajectories. Our results indicate that biomass burning might affect the atmosphere on a regional scale, contribute to regional warming, and have implications for local and regional air quality and, therefore, human health.","PeriodicalId":54279,"journal":{"name":"Elementa-Science of the Anthropocene","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66943411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1525/elementa.2022.00045
Shanru Tian, K. Smits, Younki Cho, S. Riddick, D. Zimmerle, Aidan Duggan
Methane (CH4) leakage from natural gas (NG) pipelines poses an environmental, safety, and economic threat to the public. While previous leak detection and quantification studies focus on the aboveground infrastructure, the analysis of underground NG pipeline leak scenarios is scarce. Furthermore, no data from controlled release experiments have been published on the accuracy of methods used to (1) quantify emissions from an area source and (2) use these emissions to quantify the size of a subsurface leak. This proof-of-concept work uses CH4 mole fraction, as measured by a single gas sensor, as an input to a simple dispersion-based model (WindTrax) under ideal conditions (i.e., in a field) and compares the calculated emissions to the known controlled NG release rates. The aboveground and surface CH4 mole fractions were measured for 5 days at a field testbed using controlled underground release rates ranging from 0.08 to 0.52 kg hr–1 (3.83–24.94 ft3 hr–1). Results confirmed that the mean normalized CH4 mole fraction increases as the atmosphere transitions from the Pasquill–Gifford (PG) stability class A (extremely unstable) to G (extremely stable). The estimated surface CH4 emissions showed large temporal variability, and for the emission rates tested, at least 6 h of data are needed to have a representative estimate from subsurface pipeline leaks (±27% of the controlled release rate on average). The probability that the emission estimate is within ±50% of the controlled release rate (P±50%) is approximately 50% when 1 h of data is collected; the probability approaches 100% with 3–4 h of data. Findings demonstrate the importance of providing enough data over time for accurate estimation of belowground leak scenarios. By adopting the estimation method described in this study, operators can better estimate leakage rates and identify and repair the largest leaks, thereby optimizing annual greenhouse gas emissions reductions and improving public safety.
天然气(NG)管道泄漏的甲烷(CH4)对公众造成了环境、安全和经济威胁。以往的泄漏检测和量化研究主要集中在地上基础设施上，而对地下天然气管道泄漏情景的分析很少。此外，关于(1)量化区域源排放和(2)使用这些排放来量化地下泄漏大小的方法的准确性，尚未发表来自控制释放实验的数据。这项概念验证工作使用单个气体传感器测量的CH4摩尔分数作为理想条件下(即在野外)的简单分散型模型(WindTrax)的输入，并将计算的排放量与已知的受控NG释放率进行比较。在一个现场试验台上，使用控制的地下释放速率为0.08至0.52 kg hr-1 (3.83-24.94 ft3 hr-1)，测量了5天的地上和地表CH4摩尔分数。结果证实，当大气从Pasquill-Gifford (PG)稳定等级A(极不稳定)过渡到G(极稳定)时，平均归一化CH4摩尔分数增加。估算的地表甲烷排放量表现出较大的时间变异性，对于测试的排放率，至少需要6小时的数据才能从地下管道泄漏中获得具有代表性的估计(平均为控制释放率的±27%)。当收集1 h的数据时，排放估计值在控制释放率(P±50%)的±50%以内的概率约为50%;3-4小时的数据，概率接近100%。研究结果表明，随着时间的推移，提供足够的数据对于准确估计地下泄漏情景的重要性。通过采用本研究的估算方法，运营商可以更好地估算泄漏率，识别和修复最大的泄漏，从而优化年度温室气体减排，提高公共安全。
{"title":"Estimating methane emissions from underground natural gas pipelines using an atmospheric dispersion-based method","authors":"Shanru Tian, K. Smits, Younki Cho, S. Riddick, D. Zimmerle, Aidan Duggan","doi":"10.1525/elementa.2022.00045","DOIUrl":"https://doi.org/10.1525/elementa.2022.00045","url":null,"abstract":"Methane (CH4) leakage from natural gas (NG) pipelines poses an environmental, safety, and economic threat to the public. While previous leak detection and quantification studies focus on the aboveground infrastructure, the analysis of underground NG pipeline leak scenarios is scarce. Furthermore, no data from controlled release experiments have been published on the accuracy of methods used to (1) quantify emissions from an area source and (2) use these emissions to quantify the size of a subsurface leak. This proof-of-concept work uses CH4 mole fraction, as measured by a single gas sensor, as an input to a simple dispersion-based model (WindTrax) under ideal conditions (i.e., in a field) and compares the calculated emissions to the known controlled NG release rates. The aboveground and surface CH4 mole fractions were measured for 5 days at a field testbed using controlled underground release rates ranging from 0.08 to 0.52 kg hr–1 (3.83–24.94 ft3 hr–1). Results confirmed that the mean normalized CH4 mole fraction increases as the atmosphere transitions from the Pasquill–Gifford (PG) stability class A (extremely unstable) to G (extremely stable). The estimated surface CH4 emissions showed large temporal variability, and for the emission rates tested, at least 6 h of data are needed to have a representative estimate from subsurface pipeline leaks (±27% of the controlled release rate on average). The probability that the emission estimate is within ±50% of the controlled release rate (P±50%) is approximately 50% when 1 h of data is collected; the probability approaches 100% with 3–4 h of data. Findings demonstrate the importance of providing enough data over time for accurate estimation of belowground leak scenarios. By adopting the estimation method described in this study, operators can better estimate leakage rates and identify and repair the largest leaks, thereby optimizing annual greenhouse gas emissions reductions and improving public safety.","PeriodicalId":54279,"journal":{"name":"Elementa-Science of the Anthropocene","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66943732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1525/elementa.2022.00033
G. Heinemann, Lukas Schefczyk, S. Willmes, M. Shupe
The ship-based experiment MOSAiC 2019/2020 was carried out during a full year in the Arctic and yielded an excellent data set to test the parameterizations of ocean/sea-ice/atmosphere interaction processes in regional climate models (RCMs). In the present paper, near-surface data during MOSAiC are used for the verification of the RCM COnsortium for Small-scale MOdel–Climate Limited area Mode (COSMO-CLM or CCLM). CCLM is used in a forecast mode (nested in ERA5) for the whole Arctic with 15 km resolution and is run with different configurations of sea ice data. These include the standard sea ice concentration taken from passive microwave data with around 6 km resolution, sea ice concentration from Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data and MODIS sea ice lead fraction data for the winter period. CCLM simulations show a good agreement with the measurements. Relatively large negative biases for temperature occur for November and December, which are likely associated with a too large ice thickness used by CCLM. The consideration of sea ice leads in the sub-grid parameterization in CCLM yields improved results for the near-surface temperature. ERA5 data show a large warm bias of about 2.5°C and an underestimation of the temperature variability.
{"title":"Evaluation of simulations of near-surface variables using the regional climate model CCLM for the MOSAiC winter period","authors":"G. Heinemann, Lukas Schefczyk, S. Willmes, M. Shupe","doi":"10.1525/elementa.2022.00033","DOIUrl":"https://doi.org/10.1525/elementa.2022.00033","url":null,"abstract":"The ship-based experiment MOSAiC 2019/2020 was carried out during a full year in the Arctic and yielded an excellent data set to test the parameterizations of ocean/sea-ice/atmosphere interaction processes in regional climate models (RCMs). In the present paper, near-surface data during MOSAiC are used for the verification of the RCM COnsortium for Small-scale MOdel–Climate Limited area Mode (COSMO-CLM or CCLM). CCLM is used in a forecast mode (nested in ERA5) for the whole Arctic with 15 km resolution and is run with different configurations of sea ice data. These include the standard sea ice concentration taken from passive microwave data with around 6 km resolution, sea ice concentration from Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data and MODIS sea ice lead fraction data for the winter period. CCLM simulations show a good agreement with the measurements. Relatively large negative biases for temperature occur for November and December, which are likely associated with a too large ice thickness used by CCLM. The consideration of sea ice leads in the sub-grid parameterization in CCLM yields improved results for the near-surface temperature. ERA5 data show a large warm bias of about 2.5°C and an underestimation of the temperature variability.","PeriodicalId":54279,"journal":{"name":"Elementa-Science of the Anthropocene","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66943764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1525/elementa.2022.00080
Clay S. Bell, J. Rutherford, A. Brandt, Evan D. Sherwin, T. Vaughn, D. Zimmerle
Methane detection limits, emission rate quantification accuracy, and potential cross-species interference are assessed for Bridger Photonics’ Gas Mapping LiDAR (GML) system utilizing data collected during laboratory testing and single-blind controlled release testing. Laboratory testing identified no significant interference in the path-integrated methane measurement from the gas species tested (ethylene, ethane, propane, n-butane, i-butane, and carbon dioxide). The controlled release study, comprised of 650 individual measurement passes, represents the largest dataset collected to date to characterize GML with respect to point-source emissions. Binomial regression is utilized to create detection curves illustrating the likelihood of detecting an emission of a given size under different wind conditions and for different flight altitudes. Wind-normalized methane detection limits (90% detection rate) of 0.25 (kg/h)/(m/s) and 0.41 (kg/h)/(m/s) are observed at a flight altitude of 500 feet and 675 feet above ground level, respectively. Quantification accuracy is also assessed for emissions ranging from 0.15 to 1,400 kg/h. When emission rate estimates were generated using wind from high-resolution rapid refresh (HRRR) model (the primary wind source that Bridger uses for their commercial operations), linear regression indicates bias of 8.1% (R2 = 0.89). For 95% of controlled releases above Bridger’s stated production-sector detection sensitivity (3 kg/h with 90% probability of detection), the accuracy of individual emission rate estimates produced using HRRR wind ranged from −64.1% to +87.0%. Across all controlled releases, 38.1% of estimates had error within ±20%, and 87.3% of measurements were within a factor of two (−50% to +100% error). At low wind speed (less than 2 m/s) and low emission rates (less than 3 kg/h), emission estimates are biased high, however when removed do not impact the regression significantly. The aggregate quantification error including all detected emission events was +8.2% using the HRRR wind source. The resulting detection curves and quantification accuracy illustrate important implications that must be considered when using measurements from GML or other remote emission measurement techniques to inform or validate inventory models or to audit reported emission levels from oil and gas systems.
{"title":"Single-blind determination of methane detection limits and quantification accuracy using aircraft-based LiDAR","authors":"Clay S. Bell, J. Rutherford, A. Brandt, Evan D. Sherwin, T. Vaughn, D. Zimmerle","doi":"10.1525/elementa.2022.00080","DOIUrl":"https://doi.org/10.1525/elementa.2022.00080","url":null,"abstract":"Methane detection limits, emission rate quantification accuracy, and potential cross-species interference are assessed for Bridger Photonics’ Gas Mapping LiDAR (GML) system utilizing data collected during laboratory testing and single-blind controlled release testing. Laboratory testing identified no significant interference in the path-integrated methane measurement from the gas species tested (ethylene, ethane, propane, n-butane, i-butane, and carbon dioxide). The controlled release study, comprised of 650 individual measurement passes, represents the largest dataset collected to date to characterize GML with respect to point-source emissions. Binomial regression is utilized to create detection curves illustrating the likelihood of detecting an emission of a given size under different wind conditions and for different flight altitudes. Wind-normalized methane detection limits (90% detection rate) of 0.25 (kg/h)/(m/s) and 0.41 (kg/h)/(m/s) are observed at a flight altitude of 500 feet and 675 feet above ground level, respectively. Quantification accuracy is also assessed for emissions ranging from 0.15 to 1,400 kg/h. When emission rate estimates were generated using wind from high-resolution rapid refresh (HRRR) model (the primary wind source that Bridger uses for their commercial operations), linear regression indicates bias of 8.1% (R2 = 0.89). For 95% of controlled releases above Bridger’s stated production-sector detection sensitivity (3 kg/h with 90% probability of detection), the accuracy of individual emission rate estimates produced using HRRR wind ranged from −64.1% to +87.0%. Across all controlled releases, 38.1% of estimates had error within ±20%, and 87.3% of measurements were within a factor of two (−50% to +100% error). At low wind speed (less than 2 m/s) and low emission rates (less than 3 kg/h), emission estimates are biased high, however when removed do not impact the regression significantly. The aggregate quantification error including all detected emission events was +8.2% using the HRRR wind source. The resulting detection curves and quantification accuracy illustrate important implications that must be considered when using measurements from GML or other remote emission measurement techniques to inform or validate inventory models or to audit reported emission levels from oil and gas systems.","PeriodicalId":54279,"journal":{"name":"Elementa-Science of the Anthropocene","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66944981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1525/elementa.2021.00068
M. G. Meyer, W. Gong, Sile M. Kafrissen, Olivia Torano, D. Varela, A. Santoro, N. Cassar, S. Gifford, Alexandria K. Niebergall, G. Sharpe, A. Marchetti
The NASA EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) program was established to better quantify the pathways of the biological carbon pump in order to gain a more comprehensive understanding of global carbon export efficiency. The summer 2018 field campaign in the vicinity of Ocean Station Papa (Station P; 50°N, 145°W) in the Northeast Pacific Ocean yielded evidence of low phytoplankton biomass and primary productivity dominated by small cells (<5 µm) that are reliant on recycled nutrients. Using combined 13C/15N stable isotope incubations, we calculated an average depth-integrated dissolved inorganic carbon uptake (net primary production) rate of 23.1 mmol C m–2 d–1 throughout the euphotic zone with small cells contributing 88.9% of the total daily DIC uptake. Average depth-integrated NO3– uptake rates were 1.5 mmol N m–2 d–1 with small cells contributing 73.4% of the total daily NO3– uptake. Estimates of new and regenerated production fluctuated, with small cells continuing to dominate both forms of production. The daily mixed-layer f-ratio ranged from 0.17 to 0.38 for the whole community, consistent with previous studies, which indicates a predominance of regenerated production in this region, with small and large cells (≥5 μm) having average f-ratios of 0.28 and 0.82, respectively. Peak phytoplankton biomass, total primary productivity and new production occurred between Julian Days 238 and 242 of our observation period, driven primarily by an increase in carbon and nitrate assimilation rates without apparent substantial shifts in the phytoplankton size-class structure. Our findings demonstrate the importance of small cells in performing the majority of net primary production and new production and the modest productivity fluctuations that occur in this iron-limited region of the Northeast Pacific Ocean, driven by ephemeral increases in new production, which could have significant ramifications for carbon export over broad timescales.
NASA海洋遥感出口流程(EXPORTS)项目的建立是为了更好地量化生物碳泵的路径，以便更全面地了解全球碳出口效率。2018年夏季在Papa海洋站(P站;东北太平洋50°N, 145°W)的浮游植物生物量低，初级生产力主要由依赖循环养分的小细胞(<5µm)主导。通过13C/15N稳定同位素联合培养，我们计算出整个发光区平均深度综合溶解无机碳吸收率(净初级产量)为23.1 mmol C m-2 d-1，其中小细胞贡献了88.9%的每日DIC吸收率。平均深度整合NO3 -吸收率为1.5 mmol N - m-2 - d-1，其中小细胞占每日总NO3 -吸收率的73.4%。新生产和再生生产的估计数起伏不定，小细胞继续在这两种生产形式中占主导地位。整个群落的日混合层f-比值在0.17 ~ 0.38之间，与前人的研究结果一致，表明该区域以再生生产为主，小细胞(≥5 μm)和大细胞(≥5 μm)的平均f-比值分别为0.28和0.82。浮游植物生物量、总初级生产力和新产量的峰值出现在观测期内的第238 ~ 242天，主要受碳和硝酸盐同化速率增加的驱动，浮游植物的大小级结构没有明显的实质性变化。我们的研究结果表明，小细胞在执行大部分净初级生产和新生产方面的重要性，以及在东北太平洋这个铁资源有限的地区，由于新生产的短暂增加，生产力出现适度波动，这可能对广泛时间尺度上的碳出口产生重大影响。
{"title":"Phytoplankton size-class contributions to new and regenerated production during the EXPORTS Northeast Pacific Ocean field deployment","authors":"M. G. Meyer, W. Gong, Sile M. Kafrissen, Olivia Torano, D. Varela, A. Santoro, N. Cassar, S. Gifford, Alexandria K. Niebergall, G. Sharpe, A. Marchetti","doi":"10.1525/elementa.2021.00068","DOIUrl":"https://doi.org/10.1525/elementa.2021.00068","url":null,"abstract":"The NASA EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) program was established to better quantify the pathways of the biological carbon pump in order to gain a more comprehensive understanding of global carbon export efficiency. The summer 2018 field campaign in the vicinity of Ocean Station Papa (Station P; 50°N, 145°W) in the Northeast Pacific Ocean yielded evidence of low phytoplankton biomass and primary productivity dominated by small cells (<5 µm) that are reliant on recycled nutrients. Using combined 13C/15N stable isotope incubations, we calculated an average depth-integrated dissolved inorganic carbon uptake (net primary production) rate of 23.1 mmol C m–2 d–1 throughout the euphotic zone with small cells contributing 88.9% of the total daily DIC uptake. Average depth-integrated NO3– uptake rates were 1.5 mmol N m–2 d–1 with small cells contributing 73.4% of the total daily NO3– uptake. Estimates of new and regenerated production fluctuated, with small cells continuing to dominate both forms of production. The daily mixed-layer f-ratio ranged from 0.17 to 0.38 for the whole community, consistent with previous studies, which indicates a predominance of regenerated production in this region, with small and large cells (≥5 μm) having average f-ratios of 0.28 and 0.82, respectively. Peak phytoplankton biomass, total primary productivity and new production occurred between Julian Days 238 and 242 of our observation period, driven primarily by an increase in carbon and nitrate assimilation rates without apparent substantial shifts in the phytoplankton size-class structure. Our findings demonstrate the importance of small cells in performing the majority of net primary production and new production and the modest productivity fluctuations that occur in this iron-limited region of the Northeast Pacific Ocean, driven by ephemeral increases in new production, which could have significant ramifications for carbon export over broad timescales.","PeriodicalId":54279,"journal":{"name":"Elementa-Science of the Anthropocene","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66941852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1525/elementa.2021.00101
S. Matrosov, M. Shupe, T. Uttal
This article presents the results of snowfall rate and accumulation estimates from a vertically pointing 35-GHz radar and other sensors deployed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The radar-based retrievals are the most consistent in terms of data availability and are largely immune to blowing snow. The total liquid-equivalent accumulation during the snow accumulation season is around 110 mm, with more abundant precipitation during spring months. About half of the total accumulation came from weak snowfall with rates less than approximately 0.2 mmh–1. The total snowfall estimates from a Vaisala optical sensor aboard the icebreaker are similar to those from radar retrievals, though their daily and monthly accumulations and instantaneous rates varied significantly. Compared to radar retrievals and the icebreaker optical sensor data, measurements from an identical optical sensor at an ice camp are biased high. Blowing snow effects, in part, explain differences. Weighing gauge measurements significantly overestimate snowfall during February–April 2020 as compared to other sensors and are not well suited for estimating instantaneous snowfall rates. The icebreaker optical disdrometer estimates of snowfall rates are, on average, relatively little biased compared to radar retrievals when raw particle counts are available and appropriate snowflake mass-size relations are used. These counts, however, are not available during periods that produced more than a third of the total snowfall. While there are uncertainties in the radar-based retrievals due to the choice of reflectivity-snowfall rate relations, the major error contributor is the uncertainty in the radar absolute calibration. The MOSAiC radar calibration is evaluated using comparisons with other radars and liquid water cloud–drizzle processes observed during summer. Overall, this study describes a consistent, radar-based snowfall rate product for MOSAiC that provides significant insight into Central Arctic snowfall and can be used for many other purposes.
{"title":"High temporal resolution estimates of Arctic snowfall rates emphasizing gauge and radar-based retrievals from the MOSAiC expedition","authors":"S. Matrosov, M. Shupe, T. Uttal","doi":"10.1525/elementa.2021.00101","DOIUrl":"https://doi.org/10.1525/elementa.2021.00101","url":null,"abstract":"This article presents the results of snowfall rate and accumulation estimates from a vertically pointing 35-GHz radar and other sensors deployed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The radar-based retrievals are the most consistent in terms of data availability and are largely immune to blowing snow. The total liquid-equivalent accumulation during the snow accumulation season is around 110 mm, with more abundant precipitation during spring months. About half of the total accumulation came from weak snowfall with rates less than approximately 0.2 mmh–1. The total snowfall estimates from a Vaisala optical sensor aboard the icebreaker are similar to those from radar retrievals, though their daily and monthly accumulations and instantaneous rates varied significantly. Compared to radar retrievals and the icebreaker optical sensor data, measurements from an identical optical sensor at an ice camp are biased high. Blowing snow effects, in part, explain differences. Weighing gauge measurements significantly overestimate snowfall during February–April 2020 as compared to other sensors and are not well suited for estimating instantaneous snowfall rates. The icebreaker optical disdrometer estimates of snowfall rates are, on average, relatively little biased compared to radar retrievals when raw particle counts are available and appropriate snowflake mass-size relations are used. These counts, however, are not available during periods that produced more than a third of the total snowfall. While there are uncertainties in the radar-based retrievals due to the choice of reflectivity-snowfall rate relations, the major error contributor is the uncertainty in the radar absolute calibration. The MOSAiC radar calibration is evaluated using comparisons with other radars and liquid water cloud–drizzle processes observed during summer. Overall, this study describes a consistent, radar-based snowfall rate product for MOSAiC that provides significant insight into Central Arctic snowfall and can be used for many other purposes.","PeriodicalId":54279,"journal":{"name":"Elementa-Science of the Anthropocene","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66942413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1525/elementa.2021.00121
K. Hajny, C. Floerchinger, I. Lopez-Coto, J. Pitt, C. Gately, K. Gurney, L. Hutyra, T. Jayarathne, R. Kaeser, G. Roest, M. Sargent, B. Stirm, J. Tomlin, A. J. Turner, P. Shepson, S. Wofsy
Appropriate techniques to quantify greenhouse gas emission reductions in cities over time are necessary to monitor the progress of these efforts and effectively inform continuing mitigation. We introduce a scaling factor (SF) method that combines aircraft measurements and dispersion modeling to estimate urban emissions and apply it to 9 nongrowing season research aircraft flights around New York City (NYC) in 2018–2020. This SF approach uses a weighting function to focus on an area of interest while still accounting for upwind emissions. We estimate carbon dioxide (CO2) emissions from NYC and the Greater New York Area (GNA) and compare to nested inversion analyses of the same data. The average calculated CO2 emission rates for NYC and the GNA, representative of daytime emissions for the flights, were (49 ± 16) kmol/s and (144 ± 44) kmol/s, respectively (uncertainties reported as ±1σ variability across the 9 flights). These emissions are within ∼15% of an inversion analysis and agree well with inventory estimates. By using an ensemble, we also investigate the variability introduced by several sources and find that day-to-day variability dominates the overall variability. This work investigates and demonstrates the capability of an SF method to quantify emissions specific to particular areas of interest.
{"title":"A spatially explicit inventory scaling approach to estimate urban CO2 emissions","authors":"K. Hajny, C. Floerchinger, I. Lopez-Coto, J. Pitt, C. Gately, K. Gurney, L. Hutyra, T. Jayarathne, R. Kaeser, G. Roest, M. Sargent, B. Stirm, J. Tomlin, A. J. Turner, P. Shepson, S. Wofsy","doi":"10.1525/elementa.2021.00121","DOIUrl":"https://doi.org/10.1525/elementa.2021.00121","url":null,"abstract":"Appropriate techniques to quantify greenhouse gas emission reductions in cities over time are necessary to monitor the progress of these efforts and effectively inform continuing mitigation. We introduce a scaling factor (SF) method that combines aircraft measurements and dispersion modeling to estimate urban emissions and apply it to 9 nongrowing season research aircraft flights around New York City (NYC) in 2018–2020. This SF approach uses a weighting function to focus on an area of interest while still accounting for upwind emissions. We estimate carbon dioxide (CO2) emissions from NYC and the Greater New York Area (GNA) and compare to nested inversion analyses of the same data. The average calculated CO2 emission rates for NYC and the GNA, representative of daytime emissions for the flights, were (49 ± 16) kmol/s and (144 ± 44) kmol/s, respectively (uncertainties reported as ±1σ variability across the 9 flights). These emissions are within ∼15% of an inversion analysis and agree well with inventory estimates. By using an ensemble, we also investigate the variability introduced by several sources and find that day-to-day variability dominates the overall variability. This work investigates and demonstrates the capability of an SF method to quantify emissions specific to particular areas of interest.","PeriodicalId":54279,"journal":{"name":"Elementa-Science of the Anthropocene","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66943043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1525/elementa.2022.00013
Yiyi Huang, P. Taylor, F. Rose, D. Rutan, M. Shupe, M. Webster, M. Smith
Accurate multidecadal radiative flux records are vital to understand Arctic amplification and constrain climate model uncertainties. Uncertainty in the NASA Clouds and the Earth’s Radiant Energy System (CERES)-derived irradiances is larger over sea ice than any other surface type and comes from several sources. The year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic provides a rare opportunity to explore uncertainty in CERES-derived radiative fluxes. First, a systematic and statistically robust assessment of surface shortwave and longwave fluxes was conducted using in situ measurements from MOSAiC flux stations. The CERES Synoptic 1degree (SYN1deg) product overestimates the downwelling shortwave flux by +11.40 Wm–2 and underestimates the upwelling shortwave flux by –15.70 Wm–2 and downwelling longwave fluxes by –12.58 Wm–2 at the surface during summer. In addition, large differences are found in the upwelling longwave flux when the surface approaches the melting point (approximately 0°C). The biases in downwelling shortwave and longwave fluxes suggest that the atmosphere represented in CERES is too optically thin. The large negative bias in upwelling shortwave flux can be attributed in large part to lower surface albedo (–0.15) in satellite footprint relative to surface sensors. Additionally, the results show that the spectral surface albedo used in SYN1deg overestimates albedo in visible and mid-infrared bands. A series of radiative transfer model perturbation experiments are performed to quantify the factors contributing to the differences. The CERES-MOSAiC broadband albedo differences (approximately 20 Wm–2) explain a larger portion of the upwelling shortwave flux difference than the spectral albedo shape differences (approximately 3 Wm–2). In addition, the differences between perturbation experiments using hourly and monthly MOSAiC surface albedo suggest that approximately 25% of the sea ice surface albedo variability is explained by factors not correlated with daily sea ice concentration variability. Biases in net shortwave and longwave flux can be reduced to less than half by adjusting both albedo and cloud inputs toward observed values. The results indicate that improvements in the surface albedo and cloud data would substantially reduce the uncertainty in the Arctic surface radiation budget derived from CERES data products.
{"title":"Toward a more realistic representation of surface albedo in NASA CERES-derived surface radiative fluxes","authors":"Yiyi Huang, P. Taylor, F. Rose, D. Rutan, M. Shupe, M. Webster, M. Smith","doi":"10.1525/elementa.2022.00013","DOIUrl":"https://doi.org/10.1525/elementa.2022.00013","url":null,"abstract":"Accurate multidecadal radiative flux records are vital to understand Arctic amplification and constrain climate model uncertainties. Uncertainty in the NASA Clouds and the Earth’s Radiant Energy System (CERES)-derived irradiances is larger over sea ice than any other surface type and comes from several sources. The year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic provides a rare opportunity to explore uncertainty in CERES-derived radiative fluxes. First, a systematic and statistically robust assessment of surface shortwave and longwave fluxes was conducted using in situ measurements from MOSAiC flux stations. The CERES Synoptic 1degree (SYN1deg) product overestimates the downwelling shortwave flux by +11.40 Wm–2 and underestimates the upwelling shortwave flux by –15.70 Wm–2 and downwelling longwave fluxes by –12.58 Wm–2 at the surface during summer. In addition, large differences are found in the upwelling longwave flux when the surface approaches the melting point (approximately 0°C). The biases in downwelling shortwave and longwave fluxes suggest that the atmosphere represented in CERES is too optically thin. The large negative bias in upwelling shortwave flux can be attributed in large part to lower surface albedo (–0.15) in satellite footprint relative to surface sensors. Additionally, the results show that the spectral surface albedo used in SYN1deg overestimates albedo in visible and mid-infrared bands. A series of radiative transfer model perturbation experiments are performed to quantify the factors contributing to the differences. The CERES-MOSAiC broadband albedo differences (approximately 20 Wm–2) explain a larger portion of the upwelling shortwave flux difference than the spectral albedo shape differences (approximately 3 Wm–2). In addition, the differences between perturbation experiments using hourly and monthly MOSAiC surface albedo suggest that approximately 25% of the sea ice surface albedo variability is explained by factors not correlated with daily sea ice concentration variability. Biases in net shortwave and longwave flux can be reduced to less than half by adjusting both albedo and cloud inputs toward observed values. The results indicate that improvements in the surface albedo and cloud data would substantially reduce the uncertainty in the Arctic surface radiation budget derived from CERES data products.","PeriodicalId":54279,"journal":{"name":"Elementa-Science of the Anthropocene","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66943220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1525/elementa.2022.00010
Yushan Yan, Steven Si, Weichun Zhu, Yujia Zhang
Social entrepreneurship is an important driving force for sustainable development. One existing problem with the current literature is that it is not fully clear under what conditions social entrepreneurship can promote sustainable economic, social, and environmental developments. The research evidence is even less in developing and emerging economies like China. Once an impoverished area, Yiwu has gone through a unique evolution path and developed into one of China’s top 10 wealthiest counties and a model city for sustainable development. In this study, based on a multilevel perspective and through analyzing objective statistical records and public archive data in Yiwu, we trace social entrepreneurship and sustainable development in Yiwu in recent decades. We make numerous theoretical contributions to social entrepreneurship and sustainable development literature. We identify the key factors and explore the roles of social entrepreneurship in promoting sustainable development in Yiwu. We discuss theoretical implications for social entrepreneurship specifically and entrepreneurship in general and make future research recommendations for our framework. Overall, we broaden and deepen the research on social entrepreneurship and sustainable development in an emerging economy.
{"title":"Social entrepreneurship and sustainable development: The Yiwu case","authors":"Yushan Yan, Steven Si, Weichun Zhu, Yujia Zhang","doi":"10.1525/elementa.2022.00010","DOIUrl":"https://doi.org/10.1525/elementa.2022.00010","url":null,"abstract":"Social entrepreneurship is an important driving force for sustainable development. One existing problem with the current literature is that it is not fully clear under what conditions social entrepreneurship can promote sustainable economic, social, and environmental developments. The research evidence is even less in developing and emerging economies like China. Once an impoverished area, Yiwu has gone through a unique evolution path and developed into one of China’s top 10 wealthiest counties and a model city for sustainable development. In this study, based on a multilevel perspective and through analyzing objective statistical records and public archive data in Yiwu, we trace social entrepreneurship and sustainable development in Yiwu in recent decades. We make numerous theoretical contributions to social entrepreneurship and sustainable development literature. We identify the key factors and explore the roles of social entrepreneurship in promoting sustainable development in Yiwu. We discuss theoretical implications for social entrepreneurship specifically and entrepreneurship in general and make future research recommendations for our framework. Overall, we broaden and deepen the research on social entrepreneurship and sustainable development in an emerging economy.","PeriodicalId":54279,"journal":{"name":"Elementa-Science of the Anthropocene","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66943508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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