Lorenz Curve and Gini Coefficient Reveal Hot Spots and Hot Moments for Nitrous Oxide Emissions

Saha, Debasish; Kemanian, Armen R.; Montes, Felipe; Gall, Heather E.; Adler, Paul R.; Rau, Benjamin M.

doi:10.1002/2017jg004041

Cited by 31 publications

(15 citation statements)

References 43 publications

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“…Soil heterogeneity at microscales can cause a wide range of microsite redox potentials and concentrations of substrates, which must be accounted for to explain highly skewed distributions of soil GHG fluxes (Parkin, 1987(Parkin, , 1993Savage et al, 2014;Stoyan, De-Polli, Böhm, Robertson, & Paul, 2000). Because existing ESMs are not able to represent the underlying mechanisms that control variation in enzymatic processes at microsite scales (Tian et al, 2019;Xu et al, 2016), these models often fail to capture the dynamics of soil GHG fluxes, including the so-called hot spots and hot moments (Groffman, 2012;Groffman et al, 2009;Lurndahl, 2016;Saha et al, 2018) or control points (Bernhardt et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Simultaneous numerical representation of soil microsite production and consumption of carbon dioxide, methane, and nitrous oxide using probability distribution functions

Sihi

Davidson

Savage

et al. 2019

Global Change Biology

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Production and consumption of nitrous oxide (N 2 O), methane (CH 4 ), and carbon dioxide (CO 2 ) are affected by complex interactions of temperature, moisture, and substrate supply, which are further complicated by spatial heterogeneity of the soil matrix. This microsite heterogeneity is often invoked to explain non-normal distributions of greenhouse gas (GHG) fluxes, also known as hot spots and hot moments.To advance numerical simulation of these belowground processes, we expanded the Dual Arrhenius and Michaelis-Menten model, to apply it consistently for all three GHGs with respect to the biophysical processes of production, consumption, and diffusion within the soil, including the contrasting effects of oxygen (O 2 ) as substrate or inhibitor for each process. High-frequency chamber-based measurements of all three GHGs at the Howland Forest (ME, USA) were used to parameterize the model using a multiple constraint approach. The area under a soil chamber is partitioned according to a bivariate log-normal probability distribution function (PDF) of carbon and water content across a range of microsites, which leads to a PDF of heterotrophic respiration and O 2 consumption among microsites. Linking microsite consumption of O 2 with a diffusion model generates a broad range of microsite concentrations of O 2 , which then determines the PDF of microsites that produce or consume CH 4 and N 2 O, such that a range of microsites occurs with both positive and negative signs for net CH 4 and N 2 O flux. Results demonstrate that it is numerically feasible for microsites of N 2 O reduction and CH 4 oxidation to co-occur under a single chamber, thus explaining occasional measurement of simultaneous uptake of both gases. Simultaneous simulation of all three GHGs in a parsimonious modeling framework is challenging, but it increases confidence that agreement between simulations and measurements is based on skillful numerical representation of processes across a heterogeneous environment.

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Section: Introductionmentioning

confidence: 99%

Simultaneous numerical representation of soil microsite production and consumption of carbon dioxide, methane, and nitrous oxide using probability distribution functions

Sihi

Davidson

Savage

et al. 2019

Global Change Biology

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“…Overlapping data distributions for annual soil N 2 O emissions indicate that stover removal rates and tillage practices do not appreciably impact soil N 2 O emissions. The skewness of distributions are consistent with greater variability associated with the episodic nature of high emissions events (van Kessel et al, 1993;Saha et al, 2018). Somewhat higher N 2 O emissions under ALT, particularly when stover is retained, may result from more favorable microsite conditions for soil microbial activity compared to CON tilled soils (Jin et al, 2014).…”

Section: Distributions Of Annual Soil N 2 O Emissionsmentioning

confidence: 57%

Field-to-farm gate greenhouse gas emissions from corn stover production in the Midwestern U.S.

Locker

Torkamani

Laurenzi

et al. 2019

Journal of Cleaner Production

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Measured field data were used to compare two allocation methods on life cycle greenhouse gas emissions from corn (Zea mays L.) stover production in the Midwest U.S. We used publicly-available crop yield, nitrogen fertilizer, and direct soil nitrous oxide emissions data from the USDA-ARS Resilient Economic Agricultural Practices research program. Field data were aggregated from 9 locations across 26 site-years for 3 stover harvest rates (no removal; moderate removal e 3.1 Mg ha À1 ; high removal e 7.2 Mg ha À1) and 2 tillage practices (conventional; reduced/no-till). Net carbon uptake by crops was computed from measured plant carbon content. Monte Carlo simulations sampled input distributions to assess variability in farm-to-gate GHG emissions. The base analysis assumed no change in soil organic carbon stocks. In all cases, net CO 2 uptake during crop growth and soil-respired CO 2 dominated system emissions. Emissions were most sensitive to co-product accounting method, with system expansion emissions~15% lower than mass allocation. Regardless of accounting method, lowest emissions occurred for a moderate removal rate under reduced/no-till management. The absence of correlations between N fertilization rate and stover removal rate or soil N 2 O emissions in this study challenges the use of such assumptions typically employed in life cycle assessments Storage of all carbon retained on the field as SOC could reduce emissions by an additional 15%. Our results highlight how variability in GHG emissions due to location and weather can overshadow the impact of farm management practices on field-to-farm gate emissions.

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“…The perfect equality line is the upper limit of the Lorenz curve, and a greater gap between the perfect equality line and Lorenz curve that leads to greater distribution inequality. The Gini coefficient evaluates the extent of inequality by comparing the area between the Lorenz curve and the perfect equality line ( A ) and the area under the equality line ( A + B ) (Figure 2) [47]:G=AA+B where G ranges from 0 to 1; G = 0 indicates that the risk and hospital density are in balance. The larger Gini coefficient leads to greater spatial imbalance between the risk and hospital density.…”

Section: Methodsmentioning

confidence: 99%

Health Risk and Resilience Assessment with Respect to the Main Air Pollutants in Sichuan

Xiong

Zhou

et al. 2019

IJERPH

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Rapid urbanization and industrialization in developing countries have caused an increase in air pollutant concentrations, and this has attracted public concern due to the resulting harmful effects to health. Here we present, through the spatial-temporal characteristics of six criteria air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3) in Sichuan, a human health risk assessment framework conducted to evaluate the health risk of different age groups caused by ambient air pollutants. Public health resilience was evaluated with respect to the risk resulting from ambient air pollutants, and a spatial inequality analysis between the risk caused by ambient air pollutants and hospital density in Sichuan was performed based on the Lorenz curve and Gini coefficient. The results indicated that high concentrations of PM2.5 (47.7 μg m−3) and PM10 (75.9 μg m−3) were observed in the Sichuan Basin; these two air pollutants posed a high risk to infants. The high risk caused by PM2.5 was mainly distributed in Sichuan Basin (1.14) and that caused by PM10 was principally distributed in Zigong (1.01). Additionally, the infants in Aba and Ganzi had high health resilience to the risk caused by PM2.5 (3.89 and 4.79, respectively) and PM10 (3.28 and 2.77, respectively), which was explained by the low risk in these two regions. These regions and Sichuan had severe spatial inequality between the infant hazard quotient caused by PM2.5 (G = 0.518, G = 0.493, and G = 0.456, respectively) and hospital density. This spatial inequality was also caused by PM10 (G = 0.525, G = 0.526, and G = 0.466, respectively), which is mainly attributed to the imbalance between hospital distribution and risk caused by PM2.5 (PM10) in these two areas. Such research could provide a basis for the formulation of medical construction and future air pollution control measures in Sichuan.

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Lorenz Curve and Gini Coefficient Reveal Hot Spots and Hot Moments for Nitrous Oxide Emissions

Cited by 31 publications

References 43 publications

Simultaneous numerical representation of soil microsite production and consumption of carbon dioxide, methane, and nitrous oxide using probability distribution functions

Simultaneous numerical representation of soil microsite production and consumption of carbon dioxide, methane, and nitrous oxide using probability distribution functions

Field-to-farm gate greenhouse gas emissions from corn stover production in the Midwestern U.S.

Health Risk and Resilience Assessment with Respect to the Main Air Pollutants in Sichuan

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