Simulated N2O Research Articles

Observed NO/NO2 Ratios in the Upper Troposphere Imply Errors in NO‐NO2‐O3 Cycling Kinetics or an Unaccounted NOx Reservoir

AbstractObservations from the SEAC4RS aircraft campaign over the southeast United States in August–September 2013 show NO/NO2 concentration ratios in the upper troposphere that are approximately half of photochemical equilibrium values computed from Jet Propulsion Laboratory (JPL) kinetic data. One possible explanation is the presence of labile NOx reservoir species, presumably organic, decomposing thermally to NO2 in the instrument. The NO2 instrument corrects for this artifact from known labile HNO4 and CH3O2NO2 NOx reservoirs. To bridge the gap between measured and simulated NO2, additional unaccounted labile NOx reservoir species would have to be present at a mean concentration of ~40 ppt for the SEAC4RS conditions (compared with 197 ppt for NOx). An alternative explanation is error in the low‐temperature rate constant for the NO + O3 reaction (30% 1‐σ uncertainty in JPL at 240 K) and/or in the spectroscopic data for NO2 photolysis (20% 1‐σ uncertainty). Resolving this discrepancy is important for understanding global budgets of tropospheric oxidants and for interpreting satellite observations of tropospheric NO2 columns.

No-tillage reduces long-term yield-scaled soil nitrous oxide emissions in rainfed Mediterranean agroecosystems: A field and modelling approach

There is a strong need to identify agricultural management practices that maintain agronomic productivity while diminishing soil N2O emissions. The yield-scaled N2O emissions (YSNE) indicator can help to evaluate the adequacy of a given agricultural practice under both aspects. Long-term (18-yr) soil water and mineral N dynamics, crop biomass and yields, and 2011–2012 soil N2O emissions and ancillary variables were measured on barley (Hordeum vulgare L.) production in a tillage (conventional tillage, CT; no-tillage, NT) and N rate (0, 60 and 120 kg N ha−1) combination under rainfed Mediterranean conditions (NE Spain). Once evaluated, the STICS soil-crop model was used to simulate the 18-yr soil N2O emissions of each tillage system under increasing N rates (0, 30, 60, 90 and 120 kg N ha−1) in order to identify optimum management to reduce YSNE, being initialized with observed data. Cropping season precipitation was highly variable during the experiment, being a key regulating mechanism for crop yields and simulated soil N2O emissions. Crop yield under NT with N outperformed CT in 11 years. STICS performed reasonably well when simulating cumulative N2O emissions and ancillary variables with model efficiencies greater than 0.5. The 18-yr average simulated cumulative N2O emissions were 0.50, 0.82 and 1.09 kg N2O-N ha−1 yr−1 for CT-0, CT-60 and CT-120, respectively, and they were 0.53, 0.92 and 1.19 kg N2O-N ha−1 yr−1 for their counterparts under NT. These averages mask a large variability between years, according to precipitation. The 18-yr mean yield-scaled N2O emissions were 2.8–3.3 times lower under NT, compared to the corresponding CT treatments. Under CT, N application would increase YSNE in most years while YSNE would be more resilient to the application of increasing N rates under NT. Our work demonstrates that in rainfed Mediterranean systems NT is a win-win strategy for the equilibrium between agricultural productivity and low soil N2O emissions.

Quantification of the effect of modeled lightning NO<sub>2</sub> on UV–visible air mass factors

Abstract. Space-borne measurements of tropospheric nitrogen dioxide (NO2) columns are up to 10x more sensitive to upper tropospheric (UT) NO2 than near-surface NO2 over low-reflectivity surfaces. Here, we quantify the effect of adding simulated lightning NO2 to the a priori profiles for NO2 observations from the Ozone Monitoring Instrument (OMI) using modeled NO2 profiles from the Weather Research and Forecasting–Chemistry (WRF-Chem) model. With observed NO2 profiles from the Deep Convective Clouds and Chemistry (DC3) aircraft campaign as observational truth, we quantify the bias in the NO2 column that occurs when lightning NO2 is not accounted for in the a priori profiles. Focusing on late spring and early summer in the central and eastern United States, we find that a simulation without lightning NO2 underestimates the air mass factor (AMF) by 25 % on average for common summer OMI viewing geometry and 35 % for viewing geometries that will be encountered by geostationary satellites. Using a simulation with 500 to 665 mol NO flash−1 produces good agreement with observed NO2 profiles and reduces the bias in the AMF to < ±4 % for OMI viewing geometries. The bias is regionally dependent, with the strongest effects in the southeast United States (up to 80 %) and negligible effects in the central US. We also find that constraining WRF meteorology to a reanalysis dataset reduces lightning flash counts by a factor of 2 compared to an unconstrained run, most likely due to changes in the simulated water vapor profile.

Impacts of emission reduction and meteorological conditions on air quality improvement during the 2014 Youth Olympic Games in Nanjing, China

Abstract. As the holding city of the 2nd Youth Olympic Games (YOG), Nanjing is highly industrialized and urbanized, and faces several air pollution issues. In order to ensure better air quality during the event, the local government took great efforts to control the emissions from pollutant sources. However, air quality can still be affected by synoptic weather, not only emission. In this paper, the influences of meteorological factors and emission reductions were investigated using observational data and numerical simulations with WRF–CMAQ (Weather Research and Forecasting – Community Multiscale Air Quality). During the month in which the YOG were held (August 2014), the observed hourly mean concentrations of SO2, NO2, PM10, PM2.5, CO and O3 were 11.6 µg m−3, 34.0 µg m−3, 57.8 µg m−3, 39.4 µg m−3, 0.9 mg m−3 and 38.8 µg m−3, respectively, which were below China National Ambient Air Quality Standard (level 2). However, model simulation showed that the weather conditions, such as weaker winds during the YOG, were adverse for better air quality and could increase SO2, NO2, PM10, PM2.5 and CO by 17.5, 16.9, 18.5, 18.8, 7.8 and 0.8 %. Taking account of local emission abatement only, the simulated SO2, NO2, PM10, PM2.5 and CO decreased by 24.6, 12.1, 15.1, 8.1 and 7.2 %. Consequently, stringent emission control measures can reduce the concentrations of air pollutants in the short term, and emission reduction is very important for air quality improvement during the YOG. A good example has been set for air quality protection for important social events.

N loss to drain flow and N2O emissions from a corn-soybean rotation with winter rye

Anthropogenic perturbation of the global nitrogen cycle and its effects on the environment such as hypoxia in coastal regions and increased N2O emissions is of increasing, multi-disciplinary, worldwide concern, and agricultural production is a major contributor. Only limited studies, however, have simultaneously investigated NO3− losses to subsurface drain flow and N2O emissions under corn-soybean production. We used the Root Zone Water Quality Model (RZWQM) to evaluate NO3− losses to drain flow and N2O emissions in a corn-soybean system with a winter rye cover crop (CC) in central Iowa over a nine year period. The observed and simulated average drain flow N concentration reductions from CC were 60% and 54% compared to the no cover crop system (NCC). Average annual April through October cumulative observed and simulated N2O emissions (2004–2010) were 6.7 and 6.0kgN2O-Nha−1yr−1 for NCC, and 6.2 and 7.2kgNha−1 for CC. In contrast to previous research, monthly N2O emissions were generally greatest when N loss to leaching were greatest, mostly because relatively high rainfall occurred during the months fertilizer was applied. N2O emission factors of 0.032 and 0.041 were estimated for NCC and CC using the tested model, which are similar to field results in the region. A local sensitivity analysis suggests that lower soil field capacity affects RZWQM simulations, which includes increased drain flow nitrate concentrations, increased N mineralization, and reduced soil water content. The results suggest that 1) RZWQM is a promising tool to estimate N2O emissions from subsurface drained corn-soybean rotations and to estimate the relative effects of a winter rye cover crop over a nine year period on nitrate loss to drain flow and 2) soil field capacity is an important parameter to model N mineralization and N loss to drain flow.

Simulating microbial denitrification with EPIC: Model description and evaluation

Microbial denitrification occurs in anaerobic soil microsites and aquatic environments leading to production of N2O and N2 gases, which eventually escape to the atmosphere. Atmospheric concentrations of N2O have been on the rise since the beginning of the industrial revolution due to large-scale manipulations of the N cycle in managed ecosystems, especially the use of synthetic nitrogenous fertilizer. Here we document and test a microbial denitrification model identified as IMWJ and implemented as a submodel in the EPIC terrestrial ecosystem model. The IMWJ model is resolved on an hourly time step using the concept that C oxidation releases electrons that drive a demand for electron acceptors such as O2 and oxides of N (NO3−, NO2−, and N2O). A spherical diffusion approach is used to describe O2 transport to microbial surfaces while a cylindrical diffusion method is employed to depict O2 transport to root surfaces. Oxygen uptake by microbes and roots is described with Michaelis-Menten kinetic equations. If insufficient O2 is present to accept all electrons generated, the deficit for electron acceptors may be met by oxides of nitrogen, if available. The movement of O2, CO2 and N2O through the soil profile is modeled using the gas transport equation solved on hourly or sub-hourly time steps. Bubbling equations also move N2O and N2 through the liquid phase to the soil surface under highly anaerobic conditions. We used results from a 2-yr field experiment conducted in 2007 and 2008 at a field site in southwest Michigan to test the ability of EPIC, with the IMWJ option, to capture the non-linear response of N2O fluxes as a function of increasing rates of N application to maize [Zea mays L.]. Nitrous oxide flux, soil inorganic N, and ancillary data from 2007 were used for EPIC calibration while 2008 data were used for independent model validation. Overall, EPIC reproduced well the timing and magnitude of N2O fluxes and NO3− mass in surficial soil layers after N fertilization. Although similar in magnitude, daily and cumulative simulated N2O fluxes followed a linear trend instead of the observed exponential trend. Further model testing of EPIC+IMWJ, alone or in ensembles with other models, using data from comprehensive experiments will be essential to discover areas of model improvement and increase the accuracy of N2O predictions under a wide range of environmental conditions.

Measurement and modeling of nitrous and nitric oxide emissions from a tea field in subtropical central China

Tea fields represent an important source of nitrous oxide (N2O) and nitric oxide (NO) emissions due to high nitrogen (N) fertilizer applications and very low soil pH. To investigate the temporal characteristics of N2O and NO emissions, daily emissions were measured over 2½ years period using static closed-chamber/gas chromatograph and chemiluminescent measurement system in a tea field of subtropical central China. Our results revealed that N2O and NO fluxes showed similar temporal trends, which were generally driven by temporal variations in soil temperature and soil moisture content and were also affected by fertilization events. The measured average annual N2O and NO emissions were 10.9 and 3.3 kg N ha−1 year−1, respectively, highlighting the high N2O and NO emissions from tea fields. To improve our understanding of N-cycling processes in tea ecosystems, we developed a new nitrogenous gas emission module for the water and nitrogen management model (WNMM, V2) that simulated daily N2O and NO fluxes, in which the NO was simulated as being emitted from both nitrification and nitrite chemical decomposition. The results demonstrated that the WNMM captured the general temporal dynamics of N2O (NSE = 0.40; R2 = 0.52, RMSE = 0.03 kg N ha−1 day−1, P < 0.001) and NO (NSE = 0.41; R2 = 0.44, RMSE = 0.01 kg N ha−1 day−1, P < 0.001) emissions. According to the simulation, denitrification was identified as the dominant process contributing 76.5% of the total N2O emissions, while nitrification and nitrite chemical decomposition accounted for 52.3 and 47.7% of the total NO emissions, respectively.

Measured and Modeled (DNDC) Nitrous Oxide Emissions (N2O) under Different Crop Management Practices in the Nitra Region, Slovakia

Abstract An important method of investigating N2O emissions from cropland is model simulation. The measured data of N2O emissions under conventional tillage (CT) and reduced tillage (RT) with (N1) and without (N0) N fertilizer application were used to test the DNDC model during the year 2012 (April-December) in Slovakia. There was found a good agreement with seasonal N2O emissions only for CTN0 treatment, but in case of other treatments DNDC overestimated the emissions. The relative deviation between observed and simulated total seasonal N2O emissions (kg N ha−1) from four treatments were 46%, 164%, 346% and 321% for CTN0, CTN1, RTN0 and RTN1, respectively. Also, some discrepancies were found between observed and simulated emissions when evaluating the daily N2O emissions, especially when looking at the magnitude of N2O emissions peaks. The correlation between observed and simulated daily N2O emissions (N = 38) in case of conventional tillage was quite high and significant with r = 0.48 (P &lt;0.01), r = 0.45 (P &lt;0.01) for CTN0 and CTN1 treatment, respectively. On the other hand, there was found poor correlation in reduced tillage treatment with r = 0.22 (P &gt;0.01) and r = 0.39 (P &gt;0.01), for RTN0, RTN1, respectively.

Simulating N2O emissions under different tillage systems of irrigated corn using RZ-SHAW model

Nitrous oxide (N2O) is a potent greenhouse gas (GHG), and agriculture is a global source of N2O emissions from soil fertility management. Yet emissions vary by agronomic practices and environmental factors that govern soil moisture and temperature. Ecosystem models are important tools to estimate N2O emissions by accounting for such variables, and models can strengthen field research. The objective of this study was to test RZ-SHAW predictions of crop production and N2O emissions from conventional till (CT) and no-till (NT) systems at high (HN) rate and low nitrogen input (LN) treatments in an irrigated corn (Zea mays L.) field in Colorado from 2003 to 2006 growing seasons. The model was calibrated using the HN-CT, and other treatments were used as validations. Additionally, the SHAW model was run in conjunction with RZWQM2 to account for differences in soil surface temperatures. Simulated crop yields were within 0.7 and 0.9% of measured yield for HN-NT and CT treatments, and 32 and 3% of measured yield for LN-NT and CT treatments, respectively. Spring soil temperatures were cooler by 2°C in NT compared to CT, and were correctly simulated using RZ-SHAW coupled model. RZ-SHAW simulated N2O emissions were slightly under predicted by 0.10 (1.5%) and 0.56 (7.1%) kgNha−1 for HN-NT and HN-CT treatments, respectively. Results for LN treatments showed larger differences in simulated N2O emissions and were over predicted by 0.11 (16%) kgNha−1 in NT and under predicted by 0.29 (29%) gNha−1day−1 in CT. Annual emissions were in close agreement, with observed and simulated showing 12 and 10% lower N2O emissions from HN-NT than HN-CT, respectively. Cooler surface soil temperature and higher soil water content in the HN-NT treatment caused slower breakdown of crop residue and slightly more denitrification than HN-CT, resulting in lower N2O emissions in HN-NT. This is the first test of the newly added GHG component in RZ-SHAW under no-till management, and results suggest with some improvements the model could be applied to quantify N2O emissions from different management practices.

Modelling nitrous oxide emissions from mown-grass and grain-cropping systems: Testing and sensitivity analysis of DailyDayCent using high frequency measurements

The DailyDayCent biogeochemical model was used to simulate nitrous oxide (N2O) emissions from two contrasting agro-ecosystems viz. a mown-grassland and a grain-cropping system in France. Model performance was tested using high frequency measurements over three years; additionally a local sensitivity analysis was performed. Annual N2O emissions of 1.97 and 1.24kgNha−1year−1 were simulated from mown-grassland and grain-cropland, respectively. Measured and simulated water filled pore space (r=0.86, ME=−2.5%) and soil temperature (r=0.96, ME=−0.63°C) at 10cm soil depth matched well in mown-grassland. The model predicted cumulative hay and crop production effectively. The model simulated soil mineral nitrogen (N) concentrations, particularly ammonium (NH4+), reasonably, but the model significantly underestimated soil nitrate (NO3−) concentration under both systems. In general, the model effectively simulated the dynamics and the magnitude of daily N2O flux over the whole experimental period in grain-cropland (r=0.16, ME=−0.81gNha−1day−1), with reasonable agreement between measured and modelled N2O fluxes for the mown-grassland (r=0.63, ME=−0.65gNha−1day−1). Our results indicate that DailyDayCent has potential for use as a tool for predicting overall N2O emissions in the study region. However, in-depth analysis shows some systematic discrepancies between measured and simulated N2O fluxes on a daily basis. The current exercise suggests that the DailyDayCent may need improvement, particularly the sub-module responsible for N transformations, for better simulating soil mineral N, especially soil NO3− concentration, and N2O flux on a daily basis. The sensitivity analysis shows that many factors such as climate change, N-fertilizer use, input uncertainty and parameter value could influence the simulation of N2O emissions. Sensitivity estimation also helped to identify critical parameters, which need careful estimation or site-specific calibration for successful modelling of N2O emissions in the study region.

RZWQM2 simulated management practices to mitigate climate change impacts on nitrogen losses and corn production

Gaseous and aqueous nitrogen (N) losses from corn (maize) production systems are concerns under projected climate change. In the present study, the Root Zone Water Quality Model (RZWQM2) was used to test the ability of agricultural management practices (N application rate, corn cultivar, planting date, tillage and controlled drainage) to mitigate future climate change effects on N losses and corn yield in a subsurface drained field in Iowa, USA. Under a future downscaled climate scenario, the simulated non-constant N2O emission factor (EF), yield-scaled global warming potential and N loss through drainage increased with increasing fertilization above an optimal N rate of 120 kg N ha−1. This rate represents the optimal tradeoff point between environmental issues and economic returns. While yields of the cultivar IB1068 DEKALB declined with climate change, yields of cultivar, IB 0090 GL 482 in the future climate were greater than historical yields of IB 1068 DEKALB.

Precipitation gradient and crop management affect N2O emissions: Simulation of mitigation strategies in rainfed Mediterranean conditions

In Mediterranean areas high precipitation variability and crop dependence on soil water availability make the interaction between climate and agricultural management a key issue for mitigating N2O emissions. In this study we used the STICS model to capture the effect of a water deficit gradient and precipitation variability on N2O emissions and mitigation strategies (i.e. N fertilizer type, grain legumes introduction in crop rotations and crop residues management) in a rainfed Mediterranean transect (HWD-Senés, MWD-Selvanera and LWD-Auzeville, i.e. high, medium and low water deficit, respectively). The model was first tested against a database of daily N2O fluxes measured during twelve growing seasons of winter crops at the LWD site. Several scenarios were then run on each site, always over 9 successive growing seasons to take into account precipitation variability. STICS showed a good ability to simulate the driving variables of N2O fluxes at the daily time scale. The mean observed and simulated cumulative emissions during the growing season were 0.71 and 0.82kgN2O–Nha−1, respectively. The simulated N2O emissions (mean of all scenarios) decreased with increasing water deficit being 2.51, 0.65 and 0.26kgN2O–Nha−1yr−1 for LWD-Auzeville, MWD-Selvanera and HWD-Senés, respectively, which is consistent with published results. The lower N2O emissions in the driest sites were not only related to lower fertilization rates but also to other factors associated with the Mediterranean characteristics, particularly, the drier water regime. Simulated N2O emissions were highly sensitive to the interannual variability of the climatic conditions. According to the simulations, urea fertilizer would lead to slightly higher N2O emissions (+6 and +8%) than ammonium- and calcium nitrate, respectively. The incorporation of winter pea in the traditional cereal-based Mediterranean rotations would reduce by ca. 22% the N2O emissions in HWD-Senés without changing wheat yields. Differently, in MWD-Selvanera and LWD-Auzeville, N2O emissions would remain unchanged since the emissions associated to the decomposition of low C:N ratio pea residues would counteract the lower application of N fertilizer. The systematic removal of crop residues at LWD-Auzeville would decrease the N2O emissions by 20%. However, this practice seems not recommendable if tillage is practiced due to the concomitant decrease of soil organic matter, fact that would worsen the C footprint of the system and increase the susceptibility to soil erosion. Our work highlights the interest of combining experimental and modelling approaches to account for climatic variability and evaluate long-term effects of N2O mitigation practices under Mediterranean conditions.

Simulating denitrification and nitrous oxide emissions from subtropical maize-winter wheat rotations in Southwestern China using NOEv2 model

We measured denitrification and nitrous oxide (N2O) emissions from subtropical maize-winter wheat cropping systems in the Sichuan Basin, Southwestern China between 2005 and 2006, providing a dataset that enabled us to comprehensively test the non-linear empirical NOEv2 model. The primary aims were to (i) quantify annual denitrification and N2O emissions from intensively managed agroecosystems, (ii) parameterize the denitrification sub-model in NOEv2 with site-specific measurements and incubations and (iii) evaluate NOEv2 in a subtropical area in particular. Field measurements were conducted at two adjacent sites (hereafter named NR and NF, respectively). At site NR, soils were fertilized with urea at 150 (NR-150) and 250 (NR-250) kgNha−1 crop−1. At site NF, soils were fertilized with urea (NF-U), ammonium sulphate (NF-AS) and potassium nitrate (NF-KN) at 150kgNha−1 crop−1. Set-aside soils were incubated in laboratory to determine two biological parameters of the NOEv2 model namely the potential denitrification rate (Dp) and the maximal fraction of N2O production through denitrification (rmax). With respect to subtropical climate, we modified the response function linked to soil temperature using an equation based on temperature sensitivity (Q10). The incubated Dp and rmax were 2.96kgNha−1d−1 and 0.60, respectively. A Q10 value of 2.95 was estimated from the exponential relationship between soil heterotrophic respiration rates and soil temperature (5cm depth) throughout the experimental period (r2=0.84, n=96, p=0.000). Varying across the treatments, the observed and simulated denitrification rates ranged from 10.95 to 15.38 and from 12.47 to 14.91kgNha−1yr−1, respectively. The simulations were averagely 3% larger than the observations (ranging from −20 to 27%). Accordingly, the observed and simulated N2O emissions ranged from 1.45 to 2.72 and from 1.38 to 2.21kgNha−1yr−1, respectively. The simulations were averagely 9% lower than the observations (ranging from −19 to 7%). We conclude that NOEv2 is applicable to the studied subtropical area with site-specific parameterization. Modification of the response function linked to soil temperature seems essential to apply the model to a wide range of climate conditions.

Hierarchical Bayesian calibration of nitrous oxide (N2O) and nitrogen monoxide (NO) flux module of an agro-ecosystem model: ECOSSE

With the position of nitrous oxide (N2O) being the greenhouse gas with the highest global warming potential and its long atmospheric lifetime, the anthropogenic production of N2O is of major concern. The process-based model, ECOSSE, was partly developed to quantify emissions of greenhouse gases with an input data requirement that is readily available at regional scale. Hierarchical Bayesian (HB) methods are potentially used to reduce the uncertainty and to explain the spatial variability of estimated parameters. Here, we used a Hierarchical Bayesian method to calibrate the parameters of the N2O and nitrogen monoxide (NO) sub-model of ECOSSE and to quantify the uncertainty of model simulations and to investigate the model extrapolation using soil information. The sub model simulated N2O emission from nitrification and denitrification, while the simulated NO from nitrification. The HB calibration reduced the uncertainty in the N2O and NO simulations. The model's root mean square error (RMSE) was decreased by 18% and 29% for N2O and NO across field sites compared to an uncalibrated model. Parameters for nitrification could be considered universal, while parameters for denitrification challenged the assumption that these parameters may be considered universal constant values across sites. Parameters of the NO module could be considered constant for model extrapolation to regional scale. The calibrated parameters derived from soil-specific calibration could be served as default values for the N2O module extrapolation for similar soil types. Otherwise, the mean value of posterior distribution of calibration parameters in multi-dataset could be served as the parameter for model up scaling at regional scale.

Inter-comparison of different NOX emission inventories and associated variation in simulated surface ozone in Indian region

In this work, we compare for the first time different anthropogenic NOX emission inventories and examine the associate variation in simulated surface ozone (O3) in India. Six anthropogenic NOX emission inventories namely Emission Database for Global Atmospheric Research (EDGAR), Intercontinental Chemical Transport Experiment-Phase B (INTEX-B), Regional Emission Inventory in Asia (REAS), MACCity, Indian National Emission Inventory (India_NOx), and Top-Down NOX emission inventory for India (Top-Down) are included in the comparison. We include these emission inventories in regional chemical transport model WRF-Chem to simulate tropospheric column NO2 and surface O3 mixing ratios for the month of summer (15-March to 15-April) and winter (December) in 2005. Predicted tropospheric column NO2 using different NOX emission inventory are evaluated with the OMI satellite observations. All emission inventories show similar spatial features, however uncertainty in NOX emissions distribution is about 20–50% over rural regions and about 60–160% over the major point sources. Compared to OMI, the largest bias in simulated tropospheric NO2 columns is seen in the REAS (−243.0 ± 338.8 × 1013 molecules cm−2) emission inventory, followed by EDGAR (−199.1 ± 272.2 × 1013 molecules cm−2), MACCity (−150.5 ± 236.3 × 1013 molecules cm−2), INTEX-B (−96.8 ± 199.5 × 1013 molecules cm−2), India_NOx (−87.7 ± 159.9 × 1013 molecules cm−2) and Top-Down (−30.8 ± 69.6 × 1013 molecules cm−2) inventories during winter. Simulations using different NOX emission inventories produces maximum deviation in daytime 8-h averaged O3 of the order of 9–17 ppb (15–40%) in summer and 3–12 ppb (5–25%) in winter over most of the land area. The simulation suggests that choice of NOX emission inventories have significant effect on surface O3 concentration for air quality studies over India.

The effects of spatial variability of nitrous oxide emissions from grazed pastures on the sampling distribution of chamber measurements

SUMMARYChamber sampling is a common method for measuring nitrous oxide (N2O) emissions from agricultural soils. However, for grazed pastures, the patchy nature of urine deposition results in very high levels of spatial variability in N2O emissions. In the present study, the behaviour of the sample mean was examined by simulating a large number (9999) of random N2O chamber samples under different assumptions regarding the underlying N2O distribution. Using sample sizes of up to 100 chambers, the Central Limit Theorem did not apply. The distribution of the sample mean was always right-skewed with a standard deviation varying between 12·5 and 135% of the true mean. However, the arithmetic mean was an unbiased estimator and the mean of the sample mean distribution was close to the true mean of the simulated N2O distribution. The properties of the sample mean distribution (variance, skewness) were affected significantly by the assumed distribution of the emission factor, but not by distribution of the urine patch concentration. The geometric mean was also investigated as a potential alternative estimator. However, although its distribution had lower variance, it was also biased. Two methods for bias correcting the mean were investigated. These methods reduced the bias, but at the cost of increasing the variance. Neither of the bias-corrected estimators were consistently better than the arithmetic mean in terms of skewness and variance. To improve the estimation of N2O emissions from a grazed pasture using chambers, techniques need to be developed to identify urine patch and non-urine patch areas before sampling.

Estimation and mitigation of N2O emission and nitrate leaching from intensive crop cultivation in the Haean catchment, South Korea

Considering intensive agricultural management practices and environmental conditions, the LandscapeDNDC model was applied for simulation of yields, N2O emission and nitrate leaching from major upland crops and temperate deciduous forest of the Haean catchment, South Korea. Fertilization rates were high (up to 314kgNha−1year−1) and resulted in simulated direct N2O emissions from potato, radish, soybean and cabbage fields of 1.9 and 2.1kgNha−1year−1 in 2009 and 2010, respectively. Nitrate leaching was identified as the dominant pathway of N losses in the Haean catchment with mean annual rates of 112.2 and 125.4kgNha−1year−1, causing threats to water quality and leading to substantial indirect N2O emissions of 0.84 and 0.94kgNha−1year−1 in 2009 and 2010 as estimates by applying the IPCC EF5. Simulated N2O emissions from temperate deciduous forest were low (approx. 0.50kgNha−1year−1) and predicted nitrate leaching rates were even negligible (≤0.01kgNha−1year−1). On catchment scale more than 50% of the total N2O emissions and up to 75% of nitrate leaching originated from fertilized upland fields, only covering 24% of the catchment area. Taking into account area coverage of simulated upland crops and other land uses these numbers agree well with nitrate loads calculated from discharge and concentration measurements at the catchment outlet. The change of current agricultural management practices showed a high potential of reducing N2O emission and nitrate leaching while maintaining current crop yields. Reducing (39%) and splitting N fertilizer application into 3 times was most effective and lead to about 54% and 77% reducing of N2O emission and nitrate leaching from the Haean catchment, the latter potentially contributing to improved water quality in the Soyang River Dam, which is the major source of drinking water for metropolitan residents.

Modeling nitrous oxide emissions from organic and conventional cereal-based cropping systems under different management, soil and climate factors

Mitigation of greenhouse gas emissions from agriculture should be assessed across cropping systems and agroclimatic regions. In this study, we investigate the ability of the FASSET model to analyze differences in the magnitude of N2O emissions due to soil, climate and management factors in cereal-based cropping systems. Forage maize was grown in a conventional dairy system at Mabegondo (NW Spain) and wheat and barley in organic and conventional crop rotations at Foulum (NW Denmark). These two European sites represent agricultural areas with high and low to moderate emission levels, respectively. Field trials included plots with and without catch crops that were fertilized with either mineral N fertilizer, cattle slurry, pig slurry or digested manure. Non-fertilized treatments were also included. Measurements of N2O fluxes during the growing cycle of all the crops at both sites were performed with the static chamber method with more frequent measurements post-fertilization and biweekly measurements when high fluxes were not expected. All cropping systems were simulated with the FASSET version 2.5 simulation model. Cumulative soil seasonal N2O emissions were about ten-fold higher at Mabegondo than at Foulum when averaged across systems and treatments (8.99 and 0.71kgN2O-Nha−1, respectively). The average simulated cumulative soil N2O emissions were 9.03 and 1.71kgN2O-Nha−1 at Mabegondo and at Foulum, respectively. Fertilization, catch crops and cropping systems had lower influence on the seasonal soil N2O fluxes than the environmental factors. Overall, in its current version FASSET reproduced the effects of the different factors investigated on the cumulative seasonal soil N2O emissions but temporally it overestimated emissions from nitrification and denitrification on particular days when soil operations, ploughing or fertilization, took place. The errors associated with simulated daily soil N2O fluxes increased with the magnitude of the emissions. For resolving causes of differences in simulated and measured fluxes more intensive and temporally detailed measurements of N2O fluxes and soil C and N dynamics would be needed.

The effects of household management practices on the global warming potential of urban lawns

Nitrous oxide (N2O) emissions are an important component of the greenhouse gas (GHG) budget for urban turfgrasses. A biogeochemical model DNDC successfully captured the magnitudes and patterns of N2O emissions observed at an urban turfgrass system at the Richland Creek Watershed in Nashville, TN. The model was then used to study the long-term (i.e. 75 years) impacts of lawn management practice (LMP) on soil organic carbon sequestration rate (dSOC), soil N2O emissions, and net Global Warming Potentials (net GWPs). The model simulated N2O emissions and net GWP from the three management intensity levels over 75 years ranged from 0.75 to 3.57 kg Nha−1yr−1 and 697 to 2443 kg CO2-eq ha−1yr−1, respectively, which suggested that turfgrasses act as a net carbon emitter. Reduction of fertilization is most effective to mitigate the global warming potentials of turfgrasses. Compared to the baseline scenario, halving fertilization rate and clipping recycle as an alternative to synthetic fertilizer can reduce net GWPs by 17% and 12%, respectively. In addition, reducing irrigation and mowing are also effective in lowering net GWPs. The minimum-maintenance LMP without irrigation and fertilization can reduce annual N2O emissions and net GWPs by approximately 53% and 70%, respectively, with the price of gradual depletion of soil organic carbon, when compared to the intensive-maintenance LMP. A lawn age-dependent best management practice is recommended: a high dose fertilizer input at the initial stage of lawn establishment to enhance SOC sequestration, followed by decreasing fertilization rate when the lawn ages to minimize N2O emissions. A minimum-maintained LMP with clipping recycling, and minimum irrigation and mowing, is recommended to mitigate global warming effects from urban turfgrass systems. Among all practices, clipping recycle may be a relatively malleable behavior and, therefore, a good target for interventions seeking to reduce the environmental impacts of lawn management through public education. Our results suggest that a long-term or a chronosequence study of turfgrasses with varying ages is warranted to capture the complete dynamics of contribution of turfgrasses to global warming.

A modeling approach of the relationship between nitrous oxide fluxes from soils and the water-filled pore space

Nitrous oxide (N2O) fluxes can increase significantly following small increases in soil water-filled pore space (WFPS). Thus, it is essential to improve our knowledge of this crucial relationship to better model N2O emissions by soils. We studied how much the addition of a gas transport and a gas–liquid equilibrium module to the model of N2O emissions NOE could improve simulation results. A sensitivity analysis of the modified model (NOEGTE: gas transport and equilibrium) was first performed, and then the model was tested with published data of a wetting–drying experiment. Simulated N2O fluxes plotted against WFPS appeared to be bell-shaped during the 7 days simulated, combining the effects of the low N2O production for WFPS 0.95. The WFPS generating the maximum simulated N2O fluxes shifted with time, from 0.76 after 12 h, to 0.79 after 168 h, because of an increase over time of the gas concentration gradient between the soil surface and the atmosphere. NOEGTE appeared able to capture the pattern of N2O emissions monitored in the experimental data. In particular, N2O peaks during drying were well reproduced in terms of timing, but their magnitudes were often overestimated. They were attributed to the increasing gas diffusivity and N2O exchanges from the liquid phase to the gaseous phase.