A multimedia environmental model of chemical distribution: fate, transport, and uncertainty analysis

This paper presented a framework for analysis of chemical concentration in the environment and evaluation of variance propagation within the model. This framework was illustrated through a case study of selected organic compounds of benzo[alpha]pyrene (BAP) and hexachlorobenzene (HCB) in the Great Lakes region. A multimedia environmental fate model was applied to perform stochastic simulations of chemical concentrations in various media. Both uncertainty in chemical properties and variability in hydrometeorological parameters were included in the Monte Carlo simulation, resulting in a distribution of concentrations in each medium. Parameters of compartmental dimensions, densities, emissions, and background concentrations were assumed to be constant in this study. The predicted concentrations in air, surface water and sediment were compared to reported data for validation purpose. Based on rank correlations, a sensitivity analysis was conducted to determine the influence of individual input parameters on the output variance for concentration in each environmental medium and for the basin-wide total mass inventory. Results of model validation indicated that the model predictions were in reasonable agreement with spatial distribution patterns, among the five lake basins, of reported data in the literature. For the chemical and environmental parameters given in this study, parameters associated to air-ground partitioning (such as moisture in surface soil, vapor pressure, and deposition velocity) and chemical distribution in soil solid (such as organic carbon partition coefficient and organic carbon content in root-zone soil) were targeted to reduce the uncertainty in basin-wide mass inventory. This results of sensitivity analysis in this study also indicated that the model sensitivity to an input parameter might be affected by the magnitudes of input parameters defined by the parameter settings in the simulation scenario. Therefore, uncertainty and sensitivity analyses for environmental fate models was suggested to be conducted after the model output was validated based on an appropriate input parameter settings.     

Effectiveness of porous covers for control of ammonia, reduced sulfur compounds, total hydrocarbons, selected volatile organic compounds, and odor from hog manure storage lagoons

Anaerobic lagoons are a major source of odor at concentrated animal feeding operations. Seven different kinds of artificial (geotextile and polyethylene foam) and natural (straw and redwood) permeable lagoon covers were evaluated for their potential to reduce odorous emissions generated by anaerobic waste lagoons. A novel floating sampling raft was constructed and used to simultaneously evaluate the effectiveness of lagoon covers on an operating swine waste lagoon. The air collected from the raft was evaluated for odor, total reduced sulfur (TRS) compounds, ammonia, total hydrocarbons, dimethyldisulfide, and trimethylamine. The emission rates from the lagoon were highly variable both temporally and spatially. All of the lagoon covers substantially reduced TRS emissions and odor. Geotextile fabric and a recycled foam cover exhibited the greatest reduction in total hydrocarbon emissions; natural covers were less effective. Because of consistently low emission rates of ammonia, no statistically significant reduction of ammonia emissions were observed from any of the lagoon covers.     

Effectiveness of Porous Covers for Control of Ammonia,Reduced Sulfur Compounds,Total Hydrocarbons,Selected Volatile Organic Compounds,and Odor from Hog Manure Storage Lagoons

Abstract

Anaerobic lagoons are a major source of odor at concentrated animal feeding operations. Seven different kinds of artificial (geotextile and polyethylene foam) and natural (straw and redwood) permeable lagoon covers were evaluated for their potential to reduce odorous emissions generated by anaerobic waste lagoons. A novel floating sampling raft was constructed and used to simultaneously evaluate the effectiveness of lagoon covers on an operating swine waste lagoon. The air collected from the raft was evaluated for odor, total reduced sulfur (TRS) compounds, ammonia, total hydrocarbons, dimethyldisulfide, and trimethylamine. The emission rates from the lagoon were highly variable both temporally and spatially. All of the lagoon covers substantially reduced TRS emissions and odor. Geotextile fabric and a recycled foam cover exhibited the greatest reduction in total hydrocarbon emissions; natural covers were less effective. Because of consistently low emission rates of ammonia, no statistically significant reduction of ammonia emissions were observed from any of the lagoon covers.     

Characterizing ammonia emissions from swine farms in eastern North Carolina: part 1--conventional lagoon and spray technology for waste treatment

Ammonia (NH3) fluxes from waste treatment lagoons and barns at two conventional swine farms in eastern North Carolina were measured. The waste treatment lagoon data were analyzed to elucidate the temporal (seasonal and diurnal) variability and to derive regression relationships between NH3 flux and lagoon temperature, pH and ammonium content of the lagoon, and the most relevant meteorological parameters. NH3 fluxes were measured at various sampling locations on the lagoons by a flowthrough dynamic chamber system interfaced to an environmentally controlled mobile laboratory. Two sets of open-path Fourier transform infrared (FTIR) spectrometers were also used to measure NH3 concentrations for estimating NH3 emissions from the animal housing units (barns) at the lagoon and spray technology (LST) sites. Two different types of ventilation systems were used at the two farms. Moore farm used fan ventilation, and Stokes farm used natural ventilation. The early fall and winter season intensive measurement campaigns were conducted during September 9 to October 11, 2002 (lagoon temperature ranged from 21.2 to 33.6 degrees C) and January 6 to February 2, 2003 (lagoon temperature ranged from 1.7 to 12 degrees C), respectively. Significant differences in seasonal NH3 fluxes from the waste treatment lagoons were found at both farms. Typical diurnal variation of NH3 flux with its maximum value in the afternoon was observed during both experimental periods. Exponentially increasing flux with increasing surface lagoon temperature was observed, and a linear regression relationship between logarithm of NH3 flux and lagoon surface temperature (T1) was obtained. Correlations between lagoon NH3 flux and chemical parameters, such as pH, total Kjeldahl nitrogen (TKN), and total ammoniacal nitrogen (TAN) were found to be statistically insignificant or weak. In addition to lagoon surface temperature, the difference (D) between air temperature and the lagoon surface temperature was also found to influence the NH3 flux, especially when D > 0 (i.e., air hotter than lagoon). This hot-air effect is included in the statistical-observational model obtained in this study, which was used further in the companion study (Part II), to compare the emissions from potential environmental superior technologies to evaluate the effectiveness of each technology.     

The Sensitivity of Probabilistic Risk Assessment Results to Alternative Model Structures: A Case Study of Municipal Waste Incineration

Abstract

In this analysis, human health risk due to exposure to municipal waste incinerator emissions is assessed as an example of the application of probabilistic techniques (e.g., Monte Carlo or Latin Hypercube simulations). Incinerator risk assessments are characterized by the dominance of indirect exposure, thus this analysis focuses on exposure via the ingestion of locally grown foods. In addition, since exposure to 2, 3, 7, 8-TCDD drives most incinerator risk assessments, this compound is the subject of the illustrative calculations. An important part of probabilistic risk assessment is determining the relative influence of the input parameters on the magnitude of the variance in the output distribution. This constitutes an important step toward prioritizing data needs for additional research. However, under various possible model forms reflecting incompletely understood aspects of contaminant transport, differences may be observed in estimates of risk, variance in risk, and the relative contributions of individual uncertain and variable inputs to the variance. In this analysis, a sequential structural decomposition of the relationships between the input variables is used to partition the variance in the output (i.e., risk) to identify the most influential contributors to overall variance among them. For comparison, the partitioning of variance is repeated, using techniques of multivariate regression. In summary, this study considers the degree to which results of a probabilistic assessment are contingent on critical model assumptions about the representation of deposition velocity. Specifically, this analysis assesses the impact on the results of uncertainty about the best model of the vapor/particle partitioning behavior of semi-volatile airborne pollutants.     

Estimation of PCDD/F distribution and fluxes in the Venice Lagoon,Italy: combining measurement and modelling approaches

The available experimental information on the occurrence of PCDD/Fs in the Venice Lagoon, Italy, was compiled and used to calculate fugacities for the environmental compartments of sediment, suspended particulate matter (SPM), water and air and then used to estimate fugacity ratios and assess the likely net direction of flux between media. The bottom sediment: SPM fugacity ratios for different PCDD/Fs indicate conditions close to equilibrium, suggestive of the close coupling of SPM with re-suspended sediment. Sediment/water and the sediment/air fugacity ratios suggest that net flux directions vary depending on the congener and the location within the lagoon. Net sediment-water-air movement (i.e. re-mobilisation/volatilisation) is suggested for the lighter congeners from the industrial canals, where the highest PCDD/F concentrations in the lagoon occur. The tendency to volatilise increases with decreasing congener molecular weight. In contrast, net deposition (air-water-sediment) appears to be occurring for the heaviest (hepta- and octa-) substituted PCDD/Fs. OCDF represents a marker of the industrial district of the lagoon, decreasing in concentration and as a fraction of total PCDD/Fs with increasing distance. The fugacity-based quantitative water air sediment interaction (QWASI) mass-balance model was applied to the central part of the lagoon. The key parameters for the determination of the model output, identified by a sensitivity analysis, were: the sediment active depth, the sediment re-suspension and deposition rates, and the total input of PCDD/Fs to the system. The QWASI model also indicates the tendency for the lighter PCDD/Fs to be released from surface sediment to the water column.     

Effects of the density of meteorological observations on the diagnostic wind fields and the performance of photochemical modeling in the greater Seoul area

The high ozone episode in the greater Seoul area (GSA) for the period of 27 July–1 August 1997 was modeled by the California Institute of Technology (CIT) three-dimensional photochemical model. During the period, ozone concentrations around 140 ppb were observed for 2 days. Two sets of diagnostic wind fields were constructed by using observations from the weather stations operated by the Korea Meteorological Administration. One set of wind fields utilized only observations from the surface weather stations (SWS) and the other set also utilized observations from the automatic weather stations (AWS) that were more densely distributed than the SWS. The results showed that utilizing observations from the AWS could represent fine variations in the wind field such as those caused by topography. A better wind field gave a more reasonable spatial distribution of ozone concentrations. The model performance of ozone prediction was also improved to some extent, but only marginally acceptable owing to large day-to-day variations. Overshoots of primary pollutants particularly for NO₂ were observed as pollutants were accumulated where low wind speeds were maintained. More precise information on diurnal and daily variations in emissions was warranted in order to better model the photochemical phenomena over the GSA.     

Sensitivity of modelled sulphate and nitrate aerosol to cloud,pH and ammonia emissions

A Lagrangian dispersion model has been used to predict daily sulphate aerosol in 2006 at five UK rural measurement sites and hourly nitrate aerosol in April 2003 at Harwell (UK). The sensitivity of aqueous phase sulphate production to the meteorological input has been investigated. Large differences were found between cloud fraction and cloud liquid water output from the regional and mesoscale Met Office Unified Model. The impact on the sulphate aerosol was relatively small, with the mesoscale meteorology giving better results.Sulphate aerosol production in the aqueous phase was found to be very sensitive to modelled cloud pH. As the cloud becomes acidic sulphate production is greatly limited, conversely if the cloud is basic large amounts of sulphate aerosol are produced. A fixed model pH of 5.8 was found to produce better results than allowing the model to calculate pH which resulted in large over-predictions of measured sulphate aerosol in some episodes.Nitrate aerosol was not sensitive to cloud variables or pH, but showed a slight increase with 30% more ammonia emissions, and a slight decrease with 30% less ammonia.Sulphate production in model runs with fixed pH was not sensitive to changing ammonia emissions, however the sulphate production with modelled pH was very sensitive to plus or minus 30% ammonia. This work suggests that good modelling of ammonia is essential to correct estimation of aqueous phase sulphate aerosol if cloud pH is modelled. It is concluded that modelling to estimate the effect of reduced ammonia emission scenarios on future ambient aerosol levels should also take into account the neutralising effect of ammonia in cloud and hence the effect on aqueous phase production of sulphate.     

Relating error bounds for maximum concentration estimates to diffusion meteorology uncertainty

The intent of this paper is to relate the magnitude of the error bounds of data, used as inputs to a Gaussian dispersion model, to the magnitude of the error bounds of the model output, which include the estimates of the maximum concentration and the distance to that maximum. The research specifically addresses the uncertainty in estimating the maximum concentrations from elevated buoyant sources during unstable atmospheric conditions, as these are most often of practical concern in regulatory decision making. A direct and quantitative link between the nature and magnitude of the input uncertainty and modeling results has not been previously investigated extensively. The ability to develop specific error bounds, tailored to the modeling situation, allows more informed application of the model estimates to the air quality issues.In this study, a numerical uncertainty analysis is performed using the Monte-Carlo technique to propagate the uncertainties associated with the model input. Uncertainties were assumed to exist in four model input parameters: (1) wind speed, (2) standard deviation of lateral wind direction fluctuations, (3) standard deviation of vertical wind direction fluctuations, and (4) plume rise. For each simulation, results were summarized characterizing the uncertainty in four features of the ground-level concentration pattern predicted by the model: (1) the magnitude of the maximum concentration, (2) the distance to the maximum concentration, and (3) and (4) the areas enclosed within the isopleths of 50% and 25% of the error-free estimate of maximum concentration.The authors conclude that the error bounds for the estimated maximum concentration and the distance to the maximum can be double that of the error bounds for individual model input parameters. The model output error bounds for the areas enclosed within isopleth values can be triple the error bounds of the input. It was not our intent to cover all possible combinations for the error in the input parameters. Ours was a much more limited goal of providing a lower bound estimate of model uncertainty in which we assume the input is reasonably well characterized and there is no bias in the input. These results allow estimation of minimum bounds on errors in model output when considering reasonable input error bounds.     

The use of Monte-Carlo simulation techniques for risk assessment: study of a municipal waste incinerator

The incremental lifetime risks due to polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) for the residents living in the surroundings of a municipal solid waste incinerator (MSWI) have been assessed. Two different pathways of exposure to PCDD/Fs, ingestion through the diet and exposure from MSWI emissions, were compared. Monte-Carlo simulations were carried out to obtain variability and uncertainty propagation The joint analysis of uncertainty and variability included a sensitivity analysis that identified the contribution to variance by different inputs. In general terms, PCDD/F ingestion through the diet contributed with more than 99% of the total risk, whereas direct exposition to PCDD/F emissions from the MSWI was less than 1% The results show that the median (50% percentile) of non-carcinogenic risk due to PCDD/Fs in the population living in the surroundings of the MSWI was 0.72 and the ratio of the 95th percentile and fifth percentile was about 2. With respect to the total carcinogenic risk, the median increment in individual lifetime was 7.90 x 10(-5) while the ratio between the 95th percentile and the fifth percentile was about 1.5. In this analysis, a sequential structural decomposition of the relationships between the input variables has been used to partition the variance in the output (risk) in order to identify the most influential contributors to overall variance among them.     

Modeling assessment of air emission flux of mercury from soils in terrestrial landscape components: model tests and sensitivities

The abilities of a screening-level model to predict variations in elemental mercury (Hg0) air emissions from soils in terrestrial landscapes are examined by comparing simulation results to published observational data and by performing sensitivity analyses. Despite uncertainties and simplifications, the model results obtained offer some degree of confidence in the model's joint ability to relate readily available environmental parameters to airborne emissions of Hg predicted by coupling simple atmospheric and soil parameters with Hg cycling and transport algorithms. The model reasonably predicted the observational data in the considered data sets except for one site for which significant uncertainty was associated with model input data. Predictions are consistent with many trends observed in the field studies; better predictions were obtained for nonvegetated systems (relative errors between 0.4 and 9.7%) than for shaded-soil landscapes (relative errors between 2.3 and 27%). The model reflected field data showing that daily average emission rates of Hg0, formed by the reduction of Hg(II), are primarily controlled by changes in solar radiation, soil moisture, temperature, and, to a lesser extent, wind conditions. The model may have potential use in several preliminary studies to characterize trends of airborne Hg emitted from terrestrial sources to the atmosphere.     

A passive flux denuder for evaluating emissions of ammonia at a dairy farm

Passive samplers have been shown to be an inexpensive alternative to direct sampling. Diffusion denuders have been developed to measure the concentration of species such as ammonia (NH3), which is in equilibrium with particulate ammonium nitrate. Conventional denuder sampling has required active sampling that inherently requires air pumps and, therefore, electrical power. To estimate emissions of NH3 from a fugitive source would require an array of active samplers and meteorological measurements to estimate the flux. A recently developed fabric denuder was configured in an open tube to passively sample NH3 flux. Passive and active samplers were collocated at a dairy farm at the California State University, Fresno, Agricultural Research Facility. During this comparison study, NH3 flux measurements were made at the dairy farm lagoon before and after the lagoon underwent acidification. Comparisons were made of the flux measurements obtained directly from the passive flux denuder and those calculated from an active filter pack sampler and wind velocity. The results show significant correlation between the two methods, although a correction factor needed to be applied to directly compare the two techniques. This passive sampling approach significantly reduces the cost and complexity of sampling and has the potential to economically develop a larger inventory base for ambient NH3 emissions.     

Characterizing ammonia emissions from swine farms in eastern North Carolina: part 2--potential environmentally superior technologies for waste treatment

The need for developing environmentally superior and sustainable solutions for managing the animal waste at commercial swine farms in eastern North Carolina has been recognized in recent years. Program OPEN (Odor, Pathogens, and Emissions of Nitrogen), funded by the North Carolina State University Animal and Poultry Waste Management Center (APWMC), was initiated and charged with the evaluation of potential environmentally superior technologies (ESTs) that have been developed and implemented at selected swine farms or facilities. The OPEN program has demonstrated the effectiveness of a new paradigm for policy-relevant environmental research related to North Carolina's animal waste management programs. This new paradigm is based on a commitment to improve scientific understanding associated with a wide array of environmental issues (i.e., issues related to the movement of N from animal waste into air, water, and soil media; the transmission of odor and odorants; disease-transmitting vectors; and airborne pathogens). The primary focus of this paper is on emissions of ammonia (NH3) from some potential ESTs that were being evaluated at full-scale swine facilities. During 2-week-long periods in two different seasons (warm and cold), NH3 fluxes from water-holding structures and NH3 emissions from animal houses or barns were measured at six potential EST sites: (1) Barham farm--in-ground ambient temperature anaerobic digester/energy recovery/greenhouse vegetable production system; (2) BOC #93 farm--upflow biofiltration system--EKOKAN; (3) Carrolls farm--aerobic blanket system--ISSUES-ABS; (4) Corbett #1 farm--solids separation/ gasification for energy and ash recovery centralized system--BEST; (5) Corbett #2 farm--solid separation/ reciprocating water technology--ReCip; and (6) Vestal farm--Recycling of Nutrient, Energy and Water System--ISSUES-RENEW. The ESTs were compared with similar measurements made at two conventional lagoon and spray technology (LST) farms (Moore farm and Stokes farm). A flow-through dynamic chamber system and two sets of open-path Fourier transform infrared (OP-FTIR) spectrometers measured NH3 fluxes continuously from water-holding structures and emissions from housing units at the EST and conventional LST sites. A statistical-observational model for lagoon NH3 flux was developed using a multiple linear regression analysis of 15-min averaged NH3 flux data against the relevant environmental parameters measured at the two conventional farms during two different seasons of the year. This was used to compare the water-holding structures at ESTs with those from lagoons at conventional sites under similar environmental conditions. Percentage reductions in NH3 emissions from different components of each potential EST, as well as the whole farm on which the EST was located were evaluated from the estimated emissions from water-holding structures, barns, etc., all normalized by the appropriate nitrogen excretion rate at the potential EST farm, as well as from the appropriate conventional farm. This study showed that ammonia emissions were reduced by all but one potential EST for both experimental periods. However, on the basis of our evaluation results and analysis and available information in the scientific literature, the evaluated alternative technologies may require additional technical modifications to be qualified as unconditional ESTs relative to NH3 emissions reductions.     

Measurement, analysis, and modeling of fine particulate matter in eastern North Carolina

An analysis of fine particulate data in eastern North Carolina was conducted to investigate the impact of the hog industry and its emissions of ammonia into the atmosphere. The fine particulate data are simulated using ISORROPIA, an equilibrium thermodynamic model that simulates the gas and aerosol equilibrium of inorganic atmospheric species. The observational data analyses show that the major constituents of fine particulate matter (PM2.5) are organic carbon, elemental carbon, sulfate, nitrate, and ammonium. The observed PM2.5 concentration is positively correlated with temperature but anticorrelated with wind speed. The correlation between PM2.5 and wind direction at some locations suggests an impact of ammonia emissions from hog facilities on PM2.5 formation. The modeled results are in good agreement with observations, with slightly better agreement at urban sites than at rural sites. The predicted total inorganic particulate matter (PM) concentrations are within 5% of the observed values under conditions with median initial total PM species concentrations, median relative humidity (RH), and median temperature. Ambient conditions with high PM precursor concentrations, low temperature, and high RH appear to favor the formation of secondary PM.     

Modeling studies of ammonia dispersion and dry deposition at some hog farms in North Carolina

A modeling study was conducted on dispersion and dry deposition of ammonia taking one hog farm as a unit. The ammonia emissions used in this study were measured under our OPEN (Odor, Pathogens, and Emissions of Nitrogen) project over a waste lagoon and from hog barns. Meteorological data were also collected at the farm site. The actual layout of barns and lagoons on the farms was used to simulate dry deposition downwind of the farm. Dry deposition velocity, dispersion, and dry deposition of ammonia were studied over different seasons and under different stability conditions using the short-range dispersion/air quality model, AERMOD. Dry deposition velocities were highest under near-neutral conditions and lowest under stable conditions. The highest deposition at short range occurred under nighttime stable conditions and the lowest occurred during daytime unstable conditions. Significant differences in deposition over crop and grass surfaces were observed under stable conditions.     

Sensitivity analysis for models of greenhouse gas emissions at farm level. Case study of N(2)O emissions simulated by the CERES-EGC model

Modelling complex systems such as farms often requires quantification of a large number of input factors. Sensitivity analyses are useful to reduce the number of input factors that are required to be measured or estimated accurately. Three methods of sensitivity analysis (the Morris method, the rank regression and correlation method and the Extended Fourier Amplitude Sensitivity Test method) were compared in the case of the CERES-EGC model applied to crops of a dairy farm. The qualitative Morris method provided a screening of the input factors. The two other quantitative methods were used to investigate more thoroughly the effects of input factors on output variables. Despite differences in terms of concepts and assumptions, the three methods provided similar results. Among the 44 factors under study, N₂O emissions were mainly sensitive to the fraction of N₂O emitted during denitrification, the maximum rate of nitrification, the soil bulk density and the cropland area.     

Estimating dust emission from a sandbank on the downstream Jhuoshuei River under strong wind conditions

An empirical emission model based on experimental data was proposed. The total suspended particulates (TSP) emission model obtained by using stepwise multiple-regression analysis was significant (p<0.001); moreover, the relatively high value of R-squared (R²=0.85), which indicated that four parameters included in the model (air temperature, soil silt content, soil moisture content, and wind speed) accounted for the particulate emissions from a sandbank. The results of the multiple-regression analysis demonstrated that the TSP emission factors increased with increase in air temperature, soil silt content and wind speed, but were inversely affected by the soil moisture content. The model equation verified the experimental results and proved itself to be an important tool in predicting the dust emissions from a sandbank under strong wind conditions.     

A Passive Flux Denuder for Evaluating Emissions of Ammonia at a Dairy Farm

Abstract

Passive samplers have been shown to be an inexpensive alternative to direct sampling. Diffusion denuders have been developed to measure the concentration of species such as ammonia (NH₃), which is in equilibrium with particulate ammonium nitrate. Conventional denuder sampling that inherently requires air pumps and, therefore, electrical power. To estimate emissions of NH₃ from a fugitive source would require an array of active samplers and meteorological measurements to estimate the flux. A recently developed fabric denuder was configured in an open tube to passively sample NH₃ flux. Passive and active samplers were collocated at a dairy farm at the California State University, Fresno, Agricultural Research Facility. During this comparison study, NH₃ flux measurements were made at the dairy farm lagoon before and after the lagoon underwent acidification. Comparisons were made of the flux measurements obtained directly from the passive flux denuder and those calculated from an active filter pack sampler and wind velocity. The results show significant correlation between the two methods, although a correction factor needed to be applied to directly compare the two techniques. This passive sampling approach significantly reduces the cost and complexity of sampling and has the potential to economically develop a larger inventory base for ambient NH₃ emissions.     

Spatial surrogates for the disaggregation of CORINAIR emission inventories

CORINAIR atmospheric emission inventories are frequently used input data for air quality models with a domain situated in Europe. In CORINAIR emission inventories, sources are broken down over 11 major source categories. This paper presents spatial surrogates for the disaggregation of CORINAIR atmospheric emission inventories for input of air pollutants and particulate matter to grid or polygon based air quality model domains inside Europe. The basis for the disaggregation model was the CLC2000 land cover data to which statistical weights were added. Weights were population census data for residential emissions, employment statistics for agricultural and industrial area emissions, livestock statistics for ammonia emissions and annual aircraft movements for emissions realized by air transport. Additional road and off-road network information was used to disaggregate emissions realized by traffic. A comparison of top down produced emission estimates with spatially resolved national emission data for The Netherlands and the United Kingdom gave confidence in the present spatial surrogates as a tool for the top down production of atmospheric emission maps. Explained variance at a spatial resolution of 5 km was >70% for CO, NMVOC and NO_x, >60% for PM10 and almost 50% for SO₂.     

Calculation of σz and H for an Observed Vertical Profile of Concentration

ABSTRACT

In the present work, nitrous oxide emissions were estimated [mg/L] by the use of lysimeters under the closed chamber technique for a six month period. The lysimeters were classified by the type of irrigation used: one for drinking water, and the other for treated wastewater. Each lysimeter had two different types of soil (sand and clay), based on the types of soil in Chihuahua City, Mexico. An additional classification based on the depth was done (reticular and vadose zone). Each zone collected gas by the use of a closed chamber technique, allowing the samples to be taken for subsequent quantification and analysis by gas chromatography. A statistical analysis of variance (ANOVA) and principal components analysis (PCA) were conducted to identify the most influential variables or parameters in the formation of nitrous oxide. The variables that were considered for analysis were total Kjeldahl nitrogen (TKN), ammoniacal nitrogen (NH3-N), nitrate nitrogen (NO3-N), and nitrite nitrogen (NO2-N), along with meteorological parameters. In total, 58944 mg/L of N2O were emitted during the measurement period. The results showed that concentration emissions of N2O where the type of soil is sandy were smaller than those of clay soil, while the mean concentration in the vadose zone was higher than those in the reticular zone, regardless the type of soil. The parameters that showed greater influence in the N2O emissions were NO2-N and NO3-N concentrations. Temperature also played an important role in the emissions (the highest emissions were emitted during the cold months). Furthermore, denitrification appeared to be the dominant process in the production of nitrous oxide in soils.

Implications: Nitrous oxide (N₂O) emissions produced in lysimeters with two types of soil (sand and clay) at two different depths (vadose and reticular zones) using treated waste water showed that the higher emissions of N₂O are derived from clay soils in vadose zone; it could be due to the formation of clogging that favors the formation of anoxic conditions for the denitrification process. The parameters that showed more influence in the N₂O emissions were nitrite (NO₂-N) and nitrate (NO₃-N) concentrations along with the temperature.