Estimation Of Ammonia Emissions Research Articles

Estimation of ammonia emissions from rice fields under different N inputs in China

Rice production is essential for ensuring food security. Nitrogen fertilizers applied in rice fields are prone to loss through NH3 emissions. We need to elucidate the responses of NH3 emissions to different N inputs. With a national dataset, we identified that N application rate, N application frequency, soil temperature, and soil silt fraction were the key driving factors of NH3 emissions. 190−210 kg N ha−1 was suitable for simultaneously reducing NH3 emissions and increasing rice yields. The model showed that under the actual N application rate and N application frequency of 3, national NH3 emissions were 0.852 Mt N each rice growing season, while national NH3 emission factor was 12.7 %. We developed a portable software to conveniently assess China's NH3 emissions from rice fields under different N inputs. The findings can help to optimize N inputs in rice fields to reduce NH3 emissions and improve ecosystem health and agricultural sustainability.

Impact of interannual weather variation on ammonia emissions and concentrations in Germany

Ammonia is one of the most impactful pollutants emitted from agricultural activities, harming human health and contributing to biodiversity loss. In ammonia emission inventories, the spatial distribution of annual emissions is mostly approximated by constant empirical emission fractions, which do not account for spatial variability, nor for temporal variability within a year or between years caused by weather variations. Besides, factors like manure properties, soil properties, and manure application techniques also lead to differences in the amount of ammonia emitted into the atmosphere. By not or only partly accounting for these factors, significant uncertainties are introduced into ammonia emission estimates at regional and national scales. In this study, we applied the empirical ALFAM2 model to derive spatially explicit slurry application emission fractions from cropland for use in the large-scale INTEGRATOR model, using the information on slurry properties (dry matter content and pH), manure application rate, application technique, incorporation time, air temperature, wind speed, and rainfall rate. In addition, the impact of weather on the ammonia emissions from animal housing and manure storage systems was included through a temperature-dependent scaling. We applied the method to investigate the year-to-year spatio-temporal variabilities of ammonia emissions and modeled concentrations across Germany from 2015 to 2018. Through the comparison with in situ measurements and satellite-derived observations, we studied how surface concentrations and total columns relate to local meteorology. We found that the spatio-temporal variability in emission fractions improves the ability to reproduce the interannual variability observed in ammonia concentration and total column measurements. This study shows that the developed approach to derive spatially explicit emission fractions can significantly improve ammonia emission modeling and is of great importance for studying the temporal variability between years.

Improving spatial and temporal variation of ammonia emissions for the Netherlands using livestock housing information and a Sentinel-2-derived crop map

Ammonia emissions to the atmosphere have a range of negative impacts on environmental quality, human health, and biodiversity. Despite the considerable efforts in quantifying spatially explicit ammonia emissions, there are significant uncertainties in ammonia emission estimates at regional scales. We aimed to improve the modeling of atmospheric ammonia emission variability in space and time across the Netherlands by updating an agricultural ammonia emission model with a newly derived high-resolution crop map and a livestock housing location database of the Netherlands. To generate a crop map of 12 agricultural land cover classes, we applied random forest classification to the multi-temporal multispectral observations of surface reflectance and vegetation indices derived from Sentinel-2. The crop statistics were used to calculate ammonia emission distribution based on nitrogen demand (manure and mineral fertilizer needed) of different crop types using the INTEGRATOR model. Next, the crop map was utilized to spatially allocate the ammonia emissions to a high-resolution grid across the Netherlands. In addition, ammonia emissions from livestock housing systems were introduced as point sources using location data from the Geographic Information Agricultural Business system. The temporal emission variability was updated using a recently developed TIMELINES module. After the spatial and temporal distribution of ammonia emission was obtained with the crop map and housing information, it was imported into the chemistry transport model LOTOS-EUROS to model ammonia surface concentration for validation with in situ measurements. The performed crop classification has an average accuracy score of 0.73. The derived crop map was compared with Dutch national statistics, and the results showed that the absolute median of the relative difference between Sentinel-2 derived crop areas and national statistical information is around 5%. The newly modeled ammonia monthly surface concentrations compared better with in situ measurements in terms of the magnitude and temporal variability than those derived from the original emission distribution, indicating that the temporal distribution of ammonia emissions was improved. The comparison of modeled and measured annual averaged surface concentrations illustrated that the spatial distribution of ammonia emission was also improved. All model performance indicators significantly improved, and the performance of the updated model was more stable and robust. The improvement was more evident at the stations where livestock housing is the main emission source. This study illustrates that apart from a satellite-derived crop map, information on the locations of animal housing systems also plays an essential role in better estimates of the spatial and temporal distribution of ammonia emissions. It can be worthwhile to extrapolate the method to other regions in Europe and elsewhere.

A Review: Comparison of Approaches to the Approval Process and Methodology for Estimation of Ammonia Emissions from Livestock Farms under IPPC

Ammonia (NH3) emissions have a negative impact on the welfare of breeding animals, human health, and the environment. These influences of modern intensive agriculture have led to numerous protocols, national regulations, and European Directives. Following previous regulatory measures, the Commission Implementing Decision European Union (EU) 2017/302 on 15 February 2017 has established best available technique (BAT) conclusions, under Directive 2010/75/EU of the European Parliament and the Council, for the intensive rearing of poultry and pigs. This applies to intensive poultry and pig producers with a capacity of over 40,000 poultry, 750 sows, or 2000 fattening pigs. Due to the application of this directive, air emissions have been reduced by between 40% and 75% over the last 15 years. The integrated permit monitors the entire environmental burden of the farm on its surroundings (air pollution, water, soil pollution, waste production, energy use). This review aims to provide a critical overview of how member states (including the United Kingdom) are approaching the implementation of IPPC (Integrated Pollution Prevention and Control) and the conclusions of BAT in their legislation and related documents, and how they monitor NH3 emissions from intensive livestock farming. The data for this review were obtained from 2019 to 2020.

An ensemble-variational inversion system for the estimation of ammonia emissions using CrIS satellite ammonia retrievals

Abstract. An ensemble-variational inversion system is developed for the estimation of ammonia emissions using ammonia retrievals from the Cross-track Infrared Sounder (CrIS) for use in the Global Environmental Multiscale – Modelling Air quality and Chemistry (GEM-MACH) chemical weather model. A novel hybrid method to compare logarithmic retrieval parameters to model profiles is presented. Inversions for the monthly mean ammonia emissions over North America were performed for May to August 2016. Inversions using the hybrid comparison method increased ammonia emissions at most locations within the model domain, with total monthly mean emissions increasing by 11 %–41 %. The use of these revised emissions in GEM-MACH reduced biases with surface ammonia observations by as much as 25 %. The revised ammonia emissions also improved the forecasts of total (fine + coarse) ammonium and nitrate, as well as ammonium wet deposition, with biases decreasing by as much as 13 %, but they did not improve the forecasts of just the fine components of ammonium and nitrate. A comparison of biases resulting from inversions using different comparison methods shows favourable results for the hybrid comparison method.

Improved Estimates of Ammonia Emissions from Global Croplands.

Reducing ammonia (NH3) volatilization from croplands while satisfying the food demand is strategically required to mitigate haze pollution. However, the global pattern of NH3 volatilization remains uncertain, primarily because of the episodic nature of NH3 volatilization rates and the high variation of fertilization practices. Here, we improve a global estimate of crop-specific NH3 emissions at a high spatial resolution using an updated data-driven model with a survey-based dataset of the fertilization scheme. Our estimate of the globally averaged volatilization rate (12.6% ± 2.1%) is in line with previous data-driven studies (13.7 ± 3.1%) but results in one-quarter lower emissions than process-based models (16.5 ± 3.1%). The associated global emissions are estimated at 14.4 ± 2.3 Tg N, with more than 50% of the total stemming from three stable crops or 12.2% of global harvested areas. Nearly three-quarters of global cropland-NH3 emissions could be reduced by improving fertilization schemes (right rate, right type, and right placement). A small proportion (20%) of global harvested areas, primarily located in China, India, and Pakistan, accounts for 64% of abatement potentials. Our findings provide a critical reference guide for the future abatement strategy design when considering locations and crop types.

How the Selection of Training Data and Modeling Approach Affects the Estimation of Ammonia Emissions from a Naturally Ventilated Dairy Barn—Classical Statistics versus Machine Learning

Environmental protection efforts can only be effective in the long term with a reliable quantification of pollutant gas emissions as a first step to mitigation. Measurement and analysis strategies must permit the accurate extrapolation of emission values. We systematically analyzed the added value of applying modern machine learning methods in the process of monitoring emissions from naturally ventilated livestock buildings to the atmosphere. We considered almost 40 weeks of hourly emission values from a naturally ventilated dairy cattle barn in Northern Germany. We compared model predictions using 27 different scenarios of temporal sampling, multiple measures of model accuracy, and eight different regression approaches. The error of the predicted emission values with the tested measurement protocols was, on average, well below 20%. The sensitivity of the prediction to the selected training dataset was worse for the ordinary multilinear regression. Gradient boosting and random forests provided the most accurate and robust emission value predictions, accompanied by the second-smallest model errors. Most of the highly ranked scenarios involved six measurement periods, while the scenario with the best overall performance was: One measurement period in summer and three in the transition periods, each lasting for 14 days.

Estimation of ammonia emissions from a dairy farm using a computer program

Ammonia emission tests were carried out on a dairy cow farm (100 large production units) with a slatted-floor system. The air for the tests was collected into gas washing bottles. Ammonia emissions were modeled using Operat FB software. The enthalpy of the process and the molar mass of the oxidized substance were used to calculate the calorific value of gases on the farm. The highest ammonia concentrations on the farm were obtained in the autumn and winter and amounted to 11.2 mg/m3. The highest mass of this gas in the modelling, defined as the maximum emission, was 0.85 kg/h. The annual emission was estimated at 2.78 Mg/year. These results were the basis for estimating the amount of energy (0.85 kg/h) obtainable from ammonia on the farm. The modeling of ammonia emissions applied in the study can be used to calculate the potential degree of pollution introduced into the environment and to estimate the degree of pollutants reduction.Ammonia from the farm, together with the methane emitted, can be catalytically oxidized as a potential energy source.

Sulfate formation during heavy winter haze events and the potential contribution from heterogeneous SO<sub>2</sub> + NO<sub>2</sub> reactions in the Yangtze River Delta region, China

Abstract. Rapid sulfate formation is recognized as a key characteristic of severe winter haze in China. However, air quality models tend to underestimate sulfate formation during heavy haze periods, and heterogeneous formation pathways have been proposed as promising mechanisms to reduce gaps between observation and model simulation. In this study, we implemented a reactive SO2 uptake mechanism through the SO2+NO2 heterogeneous reactions in the Comprehensive Air Quality Model with Extensions (CAMx) to improve simulation of sulfate formation in the Yangtze River Delta (YRD) region. Parameterization of the SO2+NO2 heterogeneous reactions is based on observations in Beijing and considered both the impact of relative humidity and aerosol pH on sulfate formation. Ammonia is reported to be critical for the formation of secondary inorganic aerosols. Estimation of ammonia emissions is usually associated with large uncertainties and models tend to underestimate ammonia concentrations substantially. Sensitivity tests were conducted to evaluate the influence of the SO2+NO2 heterogeneous reactions as well as ammonia emissions on modeled sulfate concentrations during a period with several heavy haze episodes in the YRD region. Base case model results show large underestimation of sulfate concentrations by 36 % under polluted conditions in the YRD region. Adding the SO2+NO2 heterogeneous reactions or doubling ammonia emissions alone leads to slight model improvement (∼6 %) on simulated sulfate concentrations in the YRD region. However, model performance significantly improved when both the SO2+NO2 heterogeneous reactions and doubled ammonia emissions were included in the simulation: predicted sulfate concentrations during polluted periods increased from 23.1 µg m−3 in the base scenario to 29.1 µg m−3 (representing an increase of 26 %). Aerosol pH is crucial for the SO2+NO2 heterogeneous reactions, and our calculated aerosol pH is always acidic and increased by 0.7 with doubled ammonia emissions. Modeling results also show that this reactive SO2 uptake mechanism enhanced sulfate simulations by 1 to 5 µg m−3 for the majority of the eastern and central parts of China, with more than 20 µg m−3 increase in sulfate concentrations over the northeastern plain. These findings suggest that the SO2+NO2 heterogeneous reactions could be potentially important for sulfate formation in the YRD region as well as other parts of China. Further studies are needed to constrain the uncertainties associated with the parameterization of the SO2+NO2 heterogeneous reactions based on local data as well as to evaluate this mechanism in other regions. In addition, ammonia emissions were found to be a key driving variable of the spatial patterns of sulfate enhancement due to the new pathway. Substantial efforts are needed to improve the accuracy of the ammonia emission inventory.

A global test of the subsidized island biogeography hypothesis

AbstractAimThe decreasing capacity of area to predict species richness on small islands (the small‐island effect; SIE) seems to be one of the few exceptions of the species–area relationship. While most studies have focused on how to detect the SIE, the underlying ecological factors determining this pattern remain largely unexplored. Here, we evaluate one of the few mechanisms proposed to explain the SIE, the subsidized island biogeography hypothesis, which posits that marine productivity around small islands may alter their expected species richness.LocationSeven hundred and ninety islands worldwide, including 420 islands < 1 km2.Time periodPresent.Major taxa studiedAngiosperms.MethodsWe applied iterative partial regression to determine the effects of island area and marine productivity on plant species richness for islands of varying sizes. We also employed geographically weighted regression to account for non‐stationarity in the marine productivity effects. Lastly, we used estimates of ammonia emissions based on nutrient excretion by seabird colonies from a subset of 66 islands to evaluate the effects of marine resources deposition on angiosperm species richness.ResultsWe found no effect of marine productivity on insular species richness, at both regional and global scales. In all models, area emerged as the only predictor of plant species richness. A weak contribution of marine productivity was only detectable in models with a low number of islands, but this effect was independent of island size. Although nutrient deposition significantly contributes to explaining plant diversity, this effect was also independent of island size.Main conclusionsOur study demonstrates that marine productivity has no general effect on plant species richness of small islands worldwide. Although marine‐derived resources may still contribute to species richness variation, this effect does not seem to be restricted to small islands. Overall, our results do not provide support for the subsidized island biogeography hypothesis.

Emission factor for atmospheric ammonia from a typical municipal wastewater treatment plant in South China

Atmospheric ammonia (NH3), a common alkaline gas found in air, plays a significant role in atmospheric chemistry, such as in the formation of secondary particles. However, large uncertainties remain in the estimation of ammonia emissions from nonagricultural sources, such as wastewater treatment plants (WWTPs). In this study, the ammonia emission factors from a large WWTP utilizing three typical biological treatment techniques to process wastewater in South China were calculated using the US EPA's WATER9 model with three years of raw sewage measurements and information about the facility. The individual emission factors calculated were 0.15 ± 0.03, 0.24 ± 0.05, 0.29 ± 0.06, and 0.25 ± 0.05 g NH3 m−3 sewage for the adsorption-biodegradation activated sludge treatment process, the UNITANK process (an upgrade of the sequencing batch reactor activated sludge treatment process), and two slightly different anaerobic-anoxic-oxic treatment processes, respectively. The overall emission factor of the WWTP was 0.24 ± 0.06 g NH3m−3 sewage. The pH of the wastewater influent is likely an important factor affecting ammonia emissions, because higher emission factors existed at higher pH values. Based on the ammonia emission factor generated in this study, sewage treatment accounted for approximately 4% of the ammonia emissions for the urban area of South China's Pearl River Delta (PRD) in 2006, which is much less than the value of 34% estimated in previous studies. To reduce the large uncertainty in the estimation of ammonia emissions in China, more field measurements are required.

Modeling of ammonia emission in the USA and EU countries using an artificial neural network approach.

Ammonia emissions at the national level are frequently estimated by applying the emission inventory approach, which includes the use of emission factors, which are difficult and expensive to determine. Emission factors are therefore the subject of estimation, and as such they contribute to inherent uncertainties in the estimation of ammonia emissions. This paper presents an alternative approach for the prediction of ammonia emissions at the national level based on artificial neural networks and broadly available sustainability and economical/agricultural indicators as model inputs. The Multilayer Perceptron (MLP) architecture was optimized using a trial-and-error procedure, including the number of hidden neurons, activation function, and a back-propagation algorithm. Principal component analysis (PCA) was applied to reduce mutual correlation between the inputs. The obtained results demonstrate that the MLP model created using the PCA transformed inputs (PCA-MLP) provides a more accurate prediction than the MLP model based on the original inputs. In the validation stage, the MLP and PCA-MLP models were tested for ammonia emission predictions for up to 2 years and compared with a principal component regression model. Among the three models, the PCA-MLP demonstrated the best performance, providing predictions for the USA and the majority of EU countries with a relative error of less than 20%.

Recent trends in gas-phase ammonia and PM2.5 ammonium in the Southeast United States

Ammonia measurements from the Southeastern Aerosol Research and Characterization (SEARCH) study network were analyzed for trends over 9 yr (2004–2012) of observations. Total ammonia concentrations, defined as the sum of gas-phase ammonia and fine particle ammonium, were found to be decreasing by 1–4% yr−1 and were qualitatively consistent with ammonia emission estimates for the SEARCH states of Alabama, Georgia, Mississippi, and Florida. On the other hand, gas-phase ammonia mixing ratios were found to be slightly rising or steady over the region, leading to the observation that the gas-phase fraction of total ammonia has steadily increased over 2004–2012 as a result of declining emissions of the strong acid precursor species sulfur dioxide (SO2) and nitrogen oxides (NOx) and consequent reduced partitioning of ammonia to the fine particle phase. Because gas-phase ammonia is removed from the atmosphere more rapidly than fine particle ammonium, an increase in the gas-phase fraction of total ammonia may result in shifted deposition patterns as more ammonia is deposited closer to sources rather than transported downwind in fine particles. Additional long-term measurements and modeling studies are needed to determine if similar transitions of total ammonia to the gas phase are occurring outside of the Southeast and to assess if these changes are impacting plants and ecosystems near major ammonia sources. Unusually high ammonia concentrations observed in 2007 in the SEARCH measurements are hypothesized to be linked to emissions from wildfires that were much more prevalent across the Southeast during that year due to elevated temperatures and widespread drought. Although wildfires are currently estimated to be a relatively small fraction (3–10%) of total ammonia emissions in the Southeast, the projected increased incidence of wildfires in this region as a result of global climate change may lead to this source’s increased importance over the rest of the 21st century.Implications: Ammonia concentrations from the Southeastern Aerosol Research and Characterization study (SEARCH) network are analyzed over the 9-yr period 2004–2012. Total ammonia (gaseous ammonia + PM2.5 ammonium) concentrations declined at a rate of 1–4% yr−1, consistent with U.S. Environmental Protection Agency (EPA) emission estimates for the Southeast United States, but the fraction of ammonia in the gas phase has risen steadily (+1–3% yr−1) over the time period. Declining emissions of SO2 and NOx resulting from imposed air quality regulations have resulted in decreased atmospheric strong acids and less ammonia partitioning to the particle phase, which may impact the amount and overall pattern of ammonia deposition.

Estimation of Ammonia Emissions from Beef Cattle Feedyards Using the Process-Based Model Manure-DNDC

<abstract><title><italic>Abstract.</italic></title> Ammonia (NH₃) emissions from beef cattle feedyards result in loss of agronomically important nitrogen (N) and can have negative environmental consequences. Both empirical and process-based models have been developed to estimate NH₃ emissions from various livestock production systems; however, little work has been conducted to assess their accuracy for large, open-lot feedyards. To validate Manure-DNDC, a process-based biogeochemical model, Manure-DNDC-simulated daily NH₃ fluxes were compared to emission data collected from two commercial feedyards, feedyard A and feedyard E, in the Texas High Plains from 2007 to 2009. Model predictions were in good agreement with observations from both feedyards (p < 0.001) and had high sensitivity to variations in air temperature and dietary crude protein (CP) concentration. Predicted mean daily NH₃ flux densities for the two feedyards had 71% to 78% agreement with observed values. In addition, Manure-DNDC estimates of annual feedyard emissions were within 3% to 15% of observations for the two feedyards, whereas a constant emission factor currently in use by the USEPA underestimated feedyard emissions by as much as 79%. The results from this study indicate that Manure-DNDC can be used to quantify average feedyard NH₃ emissions, assist with NH₃ emissions reporting, provide accurate information for legislators and policy makers, investigate methods to mitigate NH₃ loss, and help evaluate the effects of specific management practices on farm nutrient balances.

Development and uncertainty analysis of a high-resolution NH&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt; emissions inventory and its implications with precipitation over the Pearl River Delta region, China

Abstract. Detailed NH3 emission inventories are important to understand various atmospheric processes, air quality modeling studies, air pollution management, and related environmental and ecological issues. A high-resolution NH3 emission inventory was developed based on state-of-the-science techniques, up-to-date information, and advanced expert knowledge for the Pearl River Delta region, China. To provide model-ready emissions input, this NH3 emissions inventory was spatially allocated to 3 km × 3 km grid cells using source-based spatial surrogates with geographical information system (GIS) technology. For NH3 emissions, 9 source categories and 45 subcategories were identified in this region, and detailed spatial and temporal characteristics were investigated. Results show that livestock is by far the most important NH3 emission source by contributing about 61.7% of the total NH3 emissions in this region, followed by nitrogen fertilizer applications (~23.7%) and non-agricultural sources (~14.6%). Uncertainty analysis reveals that the uncertainties associated with different sources vary from source to source and the magnitude of the uncertainty associated with a specific source mainly depends on the degree of accuracy of the emission factors and activity data as well as the technique used to perform the estimate. Further studies should give priority to the hog, broiler, goose subsectors of the livestock source and N fertilizer application source in order to reduce uncertainties of ammonia emission estimates in this region. The validity of the NH3 emissions inventory is justified by the trend analysis of local precipitation compositions, such as pH values, the Ca2++NH4+/SO42−+ NO3− ratios, and NH4+ concentrations which are directly or indirectly related to NH3 emissions.

Estimation of Ammonia Emissions in Japan

Estimation of Ammonia Emissions in Japan

Ammonia in the environment: From ancient times to the present

Recent research on atmospheric ammonia has made good progress in quantifying sources/sinks and environmental impacts. This paper reviews the achievements and places them in their historical context. It considers the role of ammonia in the development of agricultural science and air chemistry, showing how these arose out of foundations in 18th century chemistry and medieval alchemy, and then identifies the original environmental sources from which the ancients obtained ammonia. Ammonia is revealed as a compound of key human interest through the centuries, with a central role played by sal ammoniac in alchemy and the emergence of modern science. The review highlights how recent environmental research has emphasized volatilization sources of ammonia. Conversely, the historical records emphasize the role of high-temperature sources, including dung burning, coal burning, naturally burning coal seams and volcanoes. Present estimates of ammonia emissions from these sources are based on few measurements, which should be a future priority.

Temporally resolved ammonia emission inventories: Current estimates, evaluation tools, and measurement needs

We evaluate the suitability of a three‐dimensional chemical transport model (CTM) as a tool for assessing ammonia emission inventories, calculate the improvement in CTM performance owing to recent advances in temporally varying ammonia emission estimates, and identify the observational data necessary to improve future ammonia emission estimates. We evaluate two advanced approaches to estimating the temporal variation in ammonia emissions: a process‐based approach and an inverse‐modeled approach. These inventories are used as inputs to a three‐dimensional CTM, PMCAMx. The model predictions of aerosol NH4+ concentration, NHx (NHx ≡ NH3 + NH4+) concentration, wet‐deposited NH4+ mass flux, and NH4+ precipitation concentration are compared with observations. However, it should be cautioned that errors in model inputs other than the ammonia emissions may bias such comparisons. We estimate the robustness of each of these amodel‐measurement comparisons as the ratio of the sensitivity to changes in emissions over the sensitivity to errors in the CTM inputs other than the ammonia emission inventory. We find the NHx concentration to be the only indicator that is sufficiently robust during all time periods. Using this as an indicator, the ammonia emission inventories with diurnal and seasonal variation improve the PMCAMx predictions in the summer and winter. In the United States, future efforts to improve the spatial and temporal accuracy of ammonia emission inventories are limited by a lack of a long‐term, widespread network of highly time‐resolved NHx measurements.

Measurement and estimation of ammonia emissions from lagoon–atmosphere interface using a coupled mass transfer and chemical reactions model, and an equilibrium model

Ammonia has recently gained importance for its increasing atmospheric concentrations and its role in the formation of aerosols. The anaerobic lagoon and spray method, commonly used for waste storage and disposal in confined animal feeding operations (CAFO), is a significant source of ammonia emissions. An accurate emission model for ammonia from aqueous surfaces can help in the development of emission factors. Data collected from field measurements made at hog waste lagoons in south eastern North Carolina, using the flow through dynamic chamber technique, were used to evaluate the Coupled mass transfer and Chemical reactions model and Equilibrium model developed by Aneja et al. [2001a. Measurement and modeling of ammonia emissions at waste treatment lagoon-Atmospheric Interface. Water, Air and Soil pollution: Focus 1, 177–188]. Sensitivity analysis shows that ammonia flux increases exponentially with lagoon temperature and pH, but a linear increase was observed with an increase in total ammoniacal nitrogen (TAN). Ammonia flux also shows a nonlinear increase with increasing wind speed. Observed ammonia fluxes were generally lower in the cold season than in the warm season when lagoon temperatures are higher. About 41% of the equilibrium model predictions and 43% of the Coupled model predictions are found to be within a factor of two of the observed fluxes. Several model performance statistics were used to evaluate the performance of the two models against the observed flux data. These indicate that the simpler Equilibrium model does as well as the Coupled model. The possible effects of the “artificial” environment within the chamber, which is different from that in the ambient atmospheric conditions above the open lagoon surface, on the measured fluxes are also recognized.

Measurement of atmospheric ammonia at a dairy using differential optical absorption spectroscopy in the mid-ultraviolet

Ammonia is the most abundant basic gas in the atmosphere, and after N 2 and N 2O is the most abundant nitrogen-containing specie (Seinfeld and Pandis, 1998. Atmospheric Chemistry and Physics: from air pollution to climate changes. Wiley, New York). Typical concentrations of ammonia in the boundary layer range from <1 part per billion by volume (ppbv) in the free continental troposphere to parts per million (ppmv) levels over animal waste lagoons and near animal stalls. Agricultural activities are the dominant global source of ammonia emissions and a major environmental concern. In the US, ≈85% of ammonia emissions come from livestock operations (EPA Trends, 1998. www.epa.gov/ttn/chief/trends98/chapter2.pdf). Dairy farms constitute a large fraction of the livestock inventory. Current estimates of ammonia emissions to the atmosphere are characterized by a high degree of uncertainty, and so it is very important to obtain better estimates of ammonia emissions. We are working at the Washington State University research dairy to quantify ammonia emissions and investigate the effects of various mitigation strategies on those emissions. We describe here a new instrument utilizing the differential optical absorption spectroscopy (DOAS) technique to measure ammonia in the mid-ultraviolet with a detectability limit of about 1 ppb. DOAS avoids many of the problems that have thwarted past ammonia concentration measurements. Initial results show concentrations in the barn/concrete yard areas in the tens of parts per million range, over the slurry lagoons of hundreds of parts per billion to low parts per million, and low parts per million levels after initial slurry applications onto pastureland. Future papers will report on emission fluxes from the various parts of the dairy and the results of mitigation strategies; we show here initial data results. For a recent review of ammonia volatilization from dairy farms, see Bussink and Oenema (Nutrient Cycling in Agroecosystems 51(1998) 19).