Greater Contribution From Agricultural Sources to Future Reactive Nitrogen Deposition in the United States

Chen, Yilin; Shen, Huizhong; Shih, Jhih‐Shyang; Russell, Armistead G.; Shao, Shuai; Hu, Yongtao; Odman, M. Talat; Nenes, Athanasios; Pavur, Gertrude K.; Zou, Yufei; Chen, Zhihong; Smith, Richard A.; Burtraw, Dallas; Driscoll, Charles T.

doi:10.1029/2019ef001453

Cited by 4 publications

(5 citation statements)

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“…NH 3 column densities retrieved from IASI on board the Metop-A satellite are assimilated to constrain spatially resolved NH 3 emissions using the 2011 NEI as the a priori inventory (Clarisse et al, 2009;Van Damme et al, 2014;USEPA, 2014). The polar sun-synchronous satellite has a 12 km diameter footprint at nadir and a bidaily global coverage.…”

Section: Iasi-nh 3 Observationsmentioning

confidence: 99%

“…Bottom-up inventories calculate the NH 3 emissions based on estimated activity levels and corresponding emission factors, both of which are subject to high uncertainties, particularly for agricultural sources, the major contributor (Cooter et al, 2012;McQuilling and Adams, 2015). Several studies have utilized NH 3 column density retrieved from the Infrared Atmospheric Sounding Interferometer (IASI) (Clarisse et al, 2009;Van Damme et al, 2015b) or the Atmospheric Infrared Sounder (AIRS; Warner et al, 2016) as well as the inferred surface mixing ratio of NH 3 from the Crosstrack Infrared Sounder (CrIS; Shephard et al, 2020) to characterize the spatiotemporal distribution of NH 3 . These satellite measurements are useful for supplementing emission inventories to identify and quantify underestimated or missing emission hotspots, especially in intensive agricultural zones (Van Damme et al, 2018;Dammers et al, 2019;Clarisse et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…This work utilizes satellite observations from the IASI-NH 3 column density measurements (IASI-NH 3 ) (Clarisse et al, 2009;Van Damme et al, 2017), to provide a highresolution, optimized NH 3 emission inventory for the US developed using an adjoint inverse modeling technique (Li et al, 2019), the robustness of which is demonstrated by evaluation against multiple independent in situ measurements. The IASI-NH 3 dataset was applied to optimize NH 3 emission estimates from the 2011 National Emissions Inventory (2011 NEI) using the Community Multiscale Air Quality Modeling System (CMAQ) and its adjoint model at a 36 km × 36 km resolution.…”

Section: Introductionmentioning

confidence: 99%

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High-resolution hybrid inversion of IASI ammonia columns to constrain US ammonia emissions using the CMAQ adjoint model

et al. 2021

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Abstract. Ammonia (NH3) emissions have large impacts on air quality and nitrogen deposition, influencing human health and the well-being of sensitive ecosystems. Large uncertainties exist in the “bottom-up” NH3 emission inventories due to limited source information and a historical lack of measurements, hindering the assessment of NH3-related environmental impacts. The increasing capability of satellites to measure NH3 abundance and the development of modeling tools enable us to better constrain NH3 emission estimates at high spatial resolution. In this study, we constrain the NH3 emission estimates from the widely used 2011 National Emissions Inventory (2011 NEI) in the US using Infrared Atmospheric Sounding Interferometer NH3 column density measurements (IASI-NH3) gridded at a 36 km by 36 km horizontal resolution. With a hybrid inverse modeling approach, we use the Community Multiscale Air Quality Modeling System (CMAQ) and its multiphase adjoint model to optimize NH3 emission estimates in April, July, and October. Our optimized emission estimates suggest that the total NH3 emissions are biased low by 26 % in 2011 NEI in April with overestimation in the Midwest and underestimation in the Southern States. In July and October, the estimates from NEI agree well with the optimized emission estimates, despite a low bias in hotspot regions. Evaluation of the inversion performance using independent observations shows reduced underestimation in simulated ambient NH3 concentration in all 3 months and reduced underestimation in NH4+ wet deposition in April. Implementing the optimized NH3 emission estimates improves the model performance in simulating PM2.5 concentration in the Midwest in April. The model results suggest that the estimated contribution of ammonium nitrate would be biased high in a priori NEI-based assessments. The higher emission estimates in this study also imply a higher ecological impact of nitrogen deposition originating from NH3 emissions.

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Section: Iasi-nh 3 Observationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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High-resolution hybrid inversion of IASI ammonia columns to constrain US ammonia emissions using the CMAQ adjoint model

et al. 2021

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“…Owing to abundant precipitation and reactive nitrogen deposition, the atmospheric wet nitrogen deposition in southwest China exceeds the average annual wet nitrogen deposition (NH 4 + -N and nitrate-N) fluxes in the country, measuring 15.8 ± 11.1 kg•N•hm −1 •yr −1 [47,48]. Hence, disparities in environmental N deposition result in variations in litter decomposition rates, and when atmospheric N deposition surpasses the capacity of forest ecosystems to retain N, N saturation transpires, consequently impeding litter decomposition, and the degree of nitrogen limitation in the local ecosystem determines this threshold [46,49]. To summarize, N addition level and environmental N deposition are two pivotal factors governing litter decomposition [50].…”

Section: Simulation Of the Effect Of Nitrogen Deposition On Litter De...mentioning

confidence: 99%

Nitrogen Deposition Modulates Litter Decomposition and Enhances Water Retention in Subtropical Forests

Xing,

Hu,

Song

et al. 2024

Forests

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Nitrogen (N) deposition influences litter decomposition and its water-holding capacity in forest ecosystems. Water conservation remains a priority, so understanding these interactions is vital for managing forests, especially in the Yunnan Plateau region. This study aimed to investigate the effects of simulated N deposition on litter decomposition and water-holding capacity in the Evergreen broad-leaf and Quercus aquifolioides forest in the central Yunnan Plateau. Indoor flooding experiments were performed alongside varied nitrogen deposition treatments. Litter decomposition rates under these treatments were evaluated using the Olson model. In the decomposition study, the N treatments in the Evergreen broad-leaved forest increased the remaining mass by 4.75%–17.50% and 2.09%–16.36% compared with the control (20.97 ± 0.44% and 42.43 ± 0.47%), while in the Quercus aquifolioides forest, the remaining mass of leaves and twigs decreased by 5.00% and 0.70% in the LN treatment compared with the control (35.47 ± 0.39% and 44.10 ± 1.18%) and the MN and HN treatments increased by 2.55%–8.13% and 5.61%–11.28%, respectively. Effects of increased N deposition on litter decomposition changed from promoting to inhibiting, as low N sped up decomposition but higher levels inhibited it. Additionally, N boosted the water-holding capacity of litter, especially in leaves. The litter from both forests displayed a notable ability to absorb water. Nitrogen deposition modulates litter decomposition and water retention properties. Specifically, high nitrogen deposition increases litter water-holding capacity by inhibiting the rate of litter decomposition, which in turn alters its mass remaining rate, lignin, and cellulose remaining rates. Efficient management of the studied forests leveraging nitrogen deposition can boost their water conservation potential, aiding in atmospheric precipitation absorption and surface runoff regulation.

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“…Additionally, 22.3% of black cherry trees in FIA plots are experiencing exceedances of their survival probability N CL (Figure 6). Many of these exceedances occur along the eastern side of the Mississippi River Basin where agricultural activities result in higher Nr deposition and in the North Carolina region where Nr deposition is high due to concentrated animal feeding operations (Chen et al, 2020). Some species (e.g., paper birch) have similar spatial distributions in probabilities of exceedance for growth rate and survival probability, while others (e.g., sugar maple) have a greater number of high probability exceedance locations for one ecological response (e.g., survival probability) but not the other.…”

Section: Spatially Varying Exceedances In N Clsmentioning

confidence: 99%

Characterizing localized nitrogen sensitivity of tree species and the associated influences of mediating factors

Coughlin,

Chang,

Craig

et al. 2024

Ecosphere

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Critical loads (CLs) are frequently used to quantify terrestrial ecosystem impacts from nitrogen (N) deposition using ecological responses such as the growth and mortality of tree species. Typically, CLs are reported as a single value, with uncertainty, for an indicator across a species' entire range. Mediating factors such as climate and soil conditions can influence species' sensitivity to N, but the magnitudes of these effects are rarely calculated explicitly. Here, we quantify the spatial variability and estimation error in N CLs for the growth and survival of 10 different tree species while accounting for key environmental factors that mediate species sensitivity to N (e.g., soil characteristics). We used a bootstrapped machine learning approach to determine the level of N deposition at which a 1% decrease occurs in growth rate or survival probability at forest plot locations across the United States. We found minimal differences (<5 kg N ha−1 year−1) when comparing a single species' CLs across climatic regimes but found considerable variability in species' local N CLs (>8.5 kg N ha−1 year−1) within these regimes. We also evaluated the most important factors for predicting tree growth rates and mortality and found that climate, competition, and air pollution generally have the greatest influence on growth rates and survival probability. Lastly, we developed a new probability of exceedance metric for each species and found high likelihoods of exceedance across large portions (46%) of some species' ranges. Our analysis demonstrates that machine learning approaches provide a unique capability to: (1) quantify mediating factor influences on N sensitivity of trees, (2) estimate the error in local N CL estimates, and (3) generate localized N CLs with probabilities of exceedance for tree species.

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Greater Contribution From Agricultural Sources to Future Reactive Nitrogen Deposition in the United States

Cited by 4 publications

References 68 publications

High-resolution hybrid inversion of IASI ammonia columns to constrain US ammonia emissions using the CMAQ adjoint model

High-resolution hybrid inversion of IASI ammonia columns to constrain US ammonia emissions using the CMAQ adjoint model

Nitrogen Deposition Modulates Litter Decomposition and Enhances Water Retention in Subtropical Forests

Characterizing localized nitrogen sensitivity of tree species and the associated influences of mediating factors

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