Inverse Dispersion Model Research Articles

Comparing NH3 emissions under different cattle housing conditions in cold regions in China with an inverse dispersion technique

Accurately assessing ammonia (NH3) emissions from animal farms is necessary for determining the effectiveness of various mitigation practices to minimize related environmental risks and maximize the potential of recycling nitrogen from manure. This study attempted to validate a hypothesis formulated using a mass-flow inventory model, which suggested that "Barn only" housing facilities without open lots might have a lower NH3 loss potential than "Barn+Lot" facilities with both dairy barns and open lots. NH3 emissions from two "Barn+Lot" dairy farms and one "Barn only" dairy farm were measured using a combination of an inverse dispersion modeling technique and an open-path laser in summer and winter. It was found that the uncertainty of emission estimates owing to the spatial variation of animal densities in the three dairy farms was 8–12%, and the total uncertainty of the NH3 emission estimates owing to spatial variation in animal densities, distortion of wind flow by animal buildings, and the inverse dispersion calculation was estimated to be less than 30%. The NH3 emission rates obtained from the "Barn only" farm (28–43 g head−1d − 1) were lower than the rates from the conventional "Barn+Lot" farms (93–184 g head−1d − 1), indicating emission mitigation of 71–76%. In comparison with the conventional dairy farms, the "Barn only" dairy farm had a lower NH3 loss potential, most likely due to the greater animal density, frequent manure removal, and lower crude protein feed. However, more field measurements of such comparisons are necessary to provide robust suggestions for mitigating environmental risks.

Measurement of real-world roadway emission rates through a fitted dispersion model

This paper presents a methodology for estimating fleet emission rates from measured roadside concentrations. By filtering measurements based on meteorological conditions, including effective wind speeds above 2m⋅s−1 and periods where the receptor is downwind, we find our simplified approach can compare well with the more sophisticated Research LINE source (RLINE) model. We applied our method to two years of roadside air pollution and traffic measurements at a Toronto, Canada, highway site to estimate minutely emission rates (ER, mass∙m−1∙s−1) and emission factors (EF, mass∙vehicle−1∙ km−1) for carbon dioxide (CO2), nitrogen oxides (NOX), carbon monoxide (CO), black carbon (BC), particulate matter mass less than 2.5 μm in diameter (PM2.5), particle number (PN), and ozone (O3) over a two-year period. Re-entering these emission rates to a multi-lane RLINE model showed favorable agreement between predicted and measured concentrations for all pollutants with 85-87% of predicted concentrations falling within a factor of two of measured. A multiple-input linear regression was used to determine light-duty vehicle (LDV) and medium/heavy-duty vehicle (MDV + HDV)-specific emission factors, which fell in or near ranges previously reported for all pollutants. More generally, the method proposed here can allow researchers to easily measure emission rates and factors from roadways using near-road concentration measurements and simple analysis methods, and can exclude some or all micrometeorological inputs, allowing researchers to perform inverse dispersion modeling in regions where such inputs are unavailable. The results also provide updated data on Canadian vehicle emissions and refine the relationships between emissions and traffic composition and speed.

Inverse AERMOD and SCIPUFF Dispersion Modeling for Farm-Level PM10 Emission Rate Assessment

HighlightsAERMOD and SCIPUFF were employed to back-calculate farm-level PM10 emission rates based on inverse modeling.Both AERMOD and SCIPUFF did not capture the diurnal and seasonal variations of farm-level PM10 emission rates.AERMOD modeling results were affected by wind speed, with higher wind speed leading to higher emission rates.Higher numbers of receptors and PM10 measurements with greater time resolution may be recommended in the future.Abstract. Air pollutant emissions from animal feeding operations (AFOs) have become a serious concern for public health and ambient air quality. Particulate matter with aerodynamic equivalent diameter less than or equal to 10 µm (PM10) is one of the major air pollutants emitted from AFOs. To assess the impacts of PM10 emissions from AFOs, knowledge about farm-level PM10 emission rates is needed but is challenging to obtain through field measurements. The inverse dispersion modeling approach provides an alternative way to estimate farm-level PM10 emission rates. In this study, two dispersion models, AERMOD and SCIPUFF, were employed to back-calculate farm-level PM10 emission rates based on hourly PM10 concentration measurements at four downwind locations in the vicinity of a commercial egg production farm in the southeast U.S. Onsite meteorological data were simultaneously recorded using a 10 m weather tower to facilitate the dispersion modeling. The modeling results were compared with PM10 emission measurements from two layer houses on the farm. Single-area source, double-area source, and double-volume source were used in AERMOD, while only single-point source was used in SCIPUFF. The inverse modeling results indicated that both SCIPUFF and AERMOD did not capture the diurnal and seasonal variations of the farm-level PM10 emission rates. In addition, the AERMOD modeling results were affected by wind speed, and higher emission rates may be predicted at higher wind speeds. The single-point source for SCIPUFF, the plume rise simplification for AERMOD, and insufficient concentration measurement resolution in response to temporal changes in wind direction may have added uncertainties to the modeling results. The results of this study suggest that more receptors covering more representative downwind locations should be considered in future modeling for farm-level emissions assessment. Moreover, ambient data collection with greater time resolution (e.g., less than one hour) is recommended to capture diurnal and seasonal patterns more rigorously. Only in this way can researchers achieve a better understanding of the effectiveness of inverse dispersion modeling for estimation of pollutant emission rates. Keywords: AERMOD, Animal feeding operations, Egg production, Farm-level emission rate, Inverse dispersion modeling, PM10, SCIPUFF.

Fugitive emissions of polycyclic aromatic compounds from an oil sands tailings pond based on fugacity and inverse dispersion flux calculations.

Alberta's oil sands tailings ponds are suspected to be a source of fugitive emissions of polycyclic aromatic compounds (PACs) to the atmosphere. Here we report, for the first time, fluxes of 6 parent and 21 alkylated PACs based on the measured co-located air and water concentrations using a two-film fugacity-based model (FUG), an inverse dispersion model (DISP) and a simple box model (BOX). Air samples were collected at the Suncor Tailings Pond 2/3 using a high volume air sampler from the "pond" and towards the pond ("non-pond") directions separately. Mean ∑27PACs in air from the "pond" direction was greater than the "non-pond" direction by a factor of 17. Water-air fugacity ratio of 20PACs quantifiable in water indicated net volatilization from water. Dispersion and box model results also indicated upward fluxes of 22PACs. Correlation between the estimated flux results of BOX and DISP model was statistically significant (r=0.99 and p<0.05), and correlation between FUG and DISP results ranged from 0.54 to 0.85. In this first-ever assessment of PAC fluxes from tailings pond, the three models confirmed volatilization fluxes of PACs indicating Suncor Tailings Pond 2/3 is a source of PAC emissions to the atmosphere. This study addressed a key data gap identified in the Joint Oil Sands Monitoring Emissions Inventory Compilation Report (Government of Alberta and Canada, 2016) which is the lack of consistent real-world tailings pond fugitive emission monitoring of organic chemicals. Our findings highlight the need for measurements from other tailings ponds to determine their overall contribution in releasing PACs to the atmosphere. This paper presents a practical method for estimating PAC emissions from other tailings ponds, which can provide a better understanding of these fugitive emissions, and thereby help to improve the overall characterization of emissions in the oil sands region.

A bayesian inference procedure based on inverse dispersion modelling for source term estimation in built-up environments

Abstract In atmospheric physics, reconstructing a pollution source is a challenging and important question. It provides better input parameters to dispersion models, and gives useful information to first-responder teams in case of an accidental toxic release. Various methods already exist, but using them requires an important amount of computational resources, especially when the accuracy of the dispersion model increases which is necessary in complex built-up environments. In this paper, a Bayesian probabilistic approach to estimate the location and the temporal emission profile of a pointwise source is proposed. More precisely, an Adaptive Multiple Importance Sampling (AMIS) algorithm is considered and enhanced by an efficient use of a Lagrangian Particle Dispersion Model (LPDM) in backward mode. Twin experiments empirically demonstrate the efficiency of the proposed inference strategy in very complex cases.

Nitrogen use efficiency and N&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;O and NH&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt; losses attributed to three fertiliser types applied to an intensively managed silage crop

Abstract. Three different nitrogen (N) fertiliser types, ammonium nitrate, urea and urea coated with a urease inhibitor (Agrotain®), were applied at standard rates (70 kg N ha−1) to experimental plots in a typical and intensively managed grassland area at the Easter Bush Farm Estate (Scotland). The nitrogen use efficiency of the fertilisers was investigated as well as nitrogen losses in the form of nitrous oxide fluxes (N2O) and ammonia (NH3) during fertilisation events in the 2016 and 2017 growing seasons. Nitrous oxide was measured by the standard static chamber technique and analysed using Bayesian statistics. Ammonia was measured using passive samplers combined with the Flux Interpretation by Dispersion and Exchange over Short Range (FIDES) inverse dispersion model. On average, fertilisation with ammonium nitrate supported the largest yields and had the highest nitrogen use efficiency, but as large spatial and seasonal variation persisted across the plots, yield differences between the three fertiliser types and zero N control were not consistent. Overall, ammonium nitrate treatment was found to increase yields significantly (p value &lt; 0.05) when compared to the urea fertilisers used in this study. Ammonium nitrate was the largest emitter of N2O (0.76 % of applied N), and the urea was the largest emitter of NH3 (16.5 % of applied N). Urea coated with a urease inhibitor did not significantly increase yields when compared to uncoated urea; however, ammonia emissions were only 10 % of the magnitude measured for the uncoated urea, and N2O emissions were only 47 % of the magnitude of those measured for ammonium nitrate fertiliser. This study suggests that urea coated with a urease inhibitor is environmentally the best choice in regards to nitrogen pollution, but because of its larger cost and lack of agronomic benefits, it is not economically attractive when compared to ammonium nitrate.

Identifying under-characterized atmospheric methane emission sources in Western Maryland

Methane (CH4), as the second largest component of greenhouse gases in the atmosphere, can be formed through biogenic or thermogenic processes. A 2015 aircraft survey conducted by the National Oceanic and Atmospheric Administration (NOAA) observed a 2.7 ppm methane plume (40% increase relative to background) at an altitude of 500 m above ground in Western Maryland. Specific sources were not attributed, however, due to rough terrain and a variety of possible sources including coal mines, landfills, and natural gas infrastructure (production wells, storage wells, abandoned wells, pipelines and compressor stations). National Energy Technology Laboratory (NETL) conducted a stationary ambient air monitoring campaign during 2014 Spring/Summer (May to August) and Fall/Winter (November to February) and a 5-day mobile sampling survey in September and October 2018 to identify the potential source(s). The ambient air monitoring laboratory was located in Western Maryland in an area containing production, abandoned, and storage wells. By using cavity ring-down spectroscopy, we observed CH4 concentrations up to 7.4 ppm. The plume events had a thermogenic δ13C signature of −35.2 ± 0.6‰. Mobile survey routes were optimized based on locations of previously identified CH4 sources. We observed a 10-min long CH4 plume event while stopping/driving in a township downwind of a compressor station and repeated 10 ppm plumes near the landfill in the northeast of Western Maryland. CH4 emissions from the landfill had a δ13C value of −54.0 ± 0.4‰, indicative of biogenic origins. We calculated the CH4 emission flux from the landfill to be 1575 ± 1173 tons/year by using inverse dispersion modeling. The flux estimation agreed with Maryland Department of the Environment inventory if assuming no day to day variation in the emission rate. CH4 sources in Western Maryland include natural gas infrastructure and the landfill. The 2.7 ppm plume observed by the aircraft was most likely from the landfill near Frostburg in the northeast of the study area.

Particulate matter emission factors using low-dust harvesters for almond nut-picking operations

ABSTRACT The lack of an available particulate matter (PM) PM2.5 emission factor for almond harvesting operations has become a challenge for particulate matter regulations and emissions inventory in California. Low-dust harvesters are viewed as one of the strategies to reduce PM emissions and help achieve the state’s PM2.5 attainment targets. This paper evaluates the potential emission reduction from using low-dust harvesters compared to the conventional. Orchard boundary measurements of PM concentrations were collected to back-calculate emission rates using inverse dispersion modeling. Emissions from four low-dust harvesters (Flory 850, Exact E3800, Weiss-McNair 9800 and Jack Rabbit) were compared to those from a conventional harvester (Flory 480) in two orchards, located in the Fresno County. Emissions of PM2.5, PM10 and total suspended particles were observed to be lower for all new harvesters compared to the conventional harvester. The range of reductions varies from about 40% to 77% in PM2.5 emissions based on emission factors generated. The average ratio of PM10 to PM2.5 emissions is about 12.5%. The results of these tests imply that these new low-dust harvesters are capable of reducing PM emissions without affecting product quality. Therefore, the San Joaquin Valley Air Pollution Control District should consider including the use of these new harvesters in the conservation management practices (CMP) for the reduction of PM emissions in the valley. Implications: The results of this research indicate that almond low-dust harvesters could potentially reduce PM emissions over traditional harvesters without any negative effect on product quality. Therefore, the use of these new harvesters should be considered as part of almond best management practices and updating of emissions inventory in the San Joaquin Valley.

Feasibility of the Inverse-Dispersion Model for Quantifying Drydock Emissions

Important processes within the shipbuilding and ship repair industry include metal cutting, welding, surface preparation, and painting. When dealing with ship repair, ships are brought into drydocks to carry out necessary repairs. Typical repairs include but are not limited to dry or wet abrasive blasting for removing the old paint and rust followed by repainting of the external hull. Also, the painting of superstructure is carried out as necessary. Additionally, many metal cutting and welding operations are carried out. Air pollutant emissions generated from repair operations carried out within drydock are challenging to quantify, particularly if some of these repair activities do not have reliable emission factors. This paper investigates the feasibility of the inverse dispersion model for quantifying drydock emissions in a shipyard environment. The authors use a well-established Gaussian dispersion model that is used as a regulatory model in the United States and many other countries in a two-step process using a code developed in MATLAB: (1) Source-to-Receptor modeling to compute ambient concentrations using assumed emissions from various sources and meteorological conditions, and (2) The utilization of the computed ambient concentrations at various receptors to compute emissions at those sources (assumed in the first step) using the inverse Gaussian code developed.

Short-term methane emissions from 2 dairy farms in California estimated by different measurement techniques and US Environmental Protection Agency inventory methodology: A case study

Reported estimates of CH4 emissions from ruminants and manure management are up to 2 times higher in atmospheric top-down calculations than in bottom-up (BU) inventories. We explored this discrepancy by estimating CH4 emissions of 2 dairy facilities in California with US Environmental Protection Agency (US EPA) methodology, which is used for BU inventories, and 3 independent measurement techniques: (1) open-path measurements with inverse dispersion modeling (hereafter open-path), (2) vehicle measurements with tracer flux ratio method, and (3) aircraft measurements with the closed-path method. All 3 techniques were used to estimate whole-facility CH4 emissions during 3 to 6 d per farm in the summer of 2016. In addition, open-path was used to estimate whole-facility CH4 emissions over 13 to 14 d per farm in the winter of 2017. Our objectives were to (1) compare whole-facility CH4 measurements utilizing the different measurement techniques, (2) compare whole-facility CH4 measurements to US EPA inventory methodology estimates, and (3) compare CH4 emissions between 2 dairies. Whole-facility CH4 estimates were similar among measurement techniques. No seasonality was detected for CH4 emissions from animal housing, but CH4 emissions from liquid manure storage were 3 to 6 times greater during the summer than during the winter measurement periods. The findings confirm previous studies showing that whole-facility CH4 emissions need to be measured throughout the year to estimate and evaluate annual inventories. Open-path measurements for liquid manure storage emissions were similar to monthly US EPA estimates during the summer, but not during the winter measurement periods. However, the numerical difference was relatively small considering yearly emission estimates. Manure CH4 emissions contributed 69 to 79% and 26 to 47% of whole-facility CH4 emissions during the summer and winter measurement periods, respectively. Methane yields from animal housing were similar between farms (on average 20.9 g of CH4/kg of dry matter intake), but CH4 emissions normalized by volatile solids (VS) loading from liquid manure storage (g of CH4 per day/kg of VS produced by all cattle per day) at 1 dairy were 1.7 and 3.5 times greater than at the other during the summer (234 vs. 137 g of CH4/kg of VS) and winter measurement periods (78 vs. 22 g of CH4/kg of VS), respectively. We attributed much of this difference to the proportion of manure stored in liquid (anaerobic) form, and suggest that manure management practices that reduce the amount of manure solids stored in liquid form could significantly reduce dairy CH4 emissions.

Source localisation and its uncertainty quantification after the third DPRK nuclear test

The International Monitoring System is being set up aiming to detect violations of the Comprehensive Nuclear-Test-Ban Treaty. Suspicious radioxenon detections were made by the International Monitoring System after the third announced nuclear test conducted by the Democratic People’s Republic of Korea (DPRK). In this paper, inverse atmospheric transport and dispersion modelling was applied to these detections, to determine the source location, the release term and its associated uncertainties. The DPRK nuclear test site was found to be a likely source location, though a second likely source region in East Asia was found by the inverse modelling, partly due to the radioxenon background from civilian sources. Therefore, techniques to indirectly assess the influence of the radioxenon background are suggested. In case of suspicious radioxenon detections after a man-made explosion, atmospheric transport and dispersion modelling is a powerful tool for assessing whether the explosion could have been nuclear or not.

Accounting for Field-Scale Dry Deposition in Backward Lagrangian Stochastic Dispersion Modelling of NH3 Emissions

A controlled ammonia (NH3) release experiment was performed at a grassland site. The aim was to quantify the effect of dry deposition between the source and the receptors (NH3 measurement locations) on emission rate estimates by means of inverse dispersion modelling. NH3 was released for three hours at a constant rate of Q = 6.29 mg s−1 from a grid of 36 orifices spread over an area of 250 m2. The increase in line-integrated NH3 concentration was measured with open-path optical miniDOAS devices at different locations downwind of the artificial source. Using a backward Lagrangian stochastic (bLS) dispersion model (bLSmodelR), the fraction of the modelled release rate to the emitted NH3 ( Q bLS / Q ) was calculated from the measurements of the individual instruments. Q bLS / Q was found to be systematically lower than 1, on average between 0.69 and 0.91, depending on the location of the receptor. We hypothesized that NH3 dry deposition to grass and soil surfaces was the main factor responsible for the observed depletion of NH3 between source and receptor. A dry deposition algorithm based on a deposition velocity approach was included in the bLS modelling. Model deposition velocities were evaluated from a ‘big-leaf’ canopy resistance analogy. Canopy resistances (generally termed R c ) that provided Q bLS / Q = 1 ranged from 75 to 290 s m−1, showing that surface removal of NH3 by dry deposition can plausibly explain the original underestimation of Q bLS / Q . The inclusion of a dry deposition process in dispersion modelling is crucial for emission estimates, which are based on concentration measurements of depositing tracers downwind of homogeneous area sources or heterogeneously-distributed hot spots, such as, e.g., urine patches on pastures in the case of NH3.

Development and implementation of a screening method to categorise the greenhouse gas mitigation potential of 91 landfills

A cost-effective screening method for assessing methane emissions was developed and employed to categorise 91 older Danish landfills into three categories defined by the magnitude of their emissions. The overall aim was to assess whether these landfills were relevant or irrelevant with respect to methane emission mitigation through the construction of biocovers. The method was based on downwind methane concentration measurements, using a van-mounted cavity ring-down spectrometer combined with inverse dispersion modelling to estimate whole-site methane emission rates. This method was found to be less accurate than the more labour-intensive tracer gas dispersion method, and therefore cannot be recommended if a high degree of accuracy is required. However, it is useful if a less accurate examination is sufficient. A sensitivity analysis showed the dispersion model used to be highly sensitive to variations in input parameters. Of the 91 landfills in the survey, 25 were found to be relevant for biocover construction when the methane emission threshold was set at 2 kg CH4 h−1.

Ammonia Emissions from Dairy Lagoons in the Western U.S.

Abstract. Ammonia (NH3) emissions from dairy liquid storage systems can be a source of reactive nitrogen (N) released to the environment, with a potential to adversely affect sensitive ecosystems and human health. However, little on-farm research has been conducted to estimate these emissions and determine the factors that may affect these emissions. Six lagoons in south-central Idaho were monitored for one year using open-path Fourier transform spectrometry, with NH3 emissions estimated using inverse dispersion modeling (WindTrax software). Lagoon physicochemical characteristics thought to contribute to NH3 emissions were also monitored over this period. Average total emissions from the lagoons ranged from 12 to 43 kg NH3 ha-1 d-1, or 5.4 to 85 kg NH3 d-1. Emissions from the settling basin on one dairy were 30% of the total emissions from the liquid storage system, indicating that basins are important sources of on-farm NH3 emissions. Emissions generally trended greater during the summer, when temperatures were greater. High wind events and agitation of the lagoons created temporary increases in NH3 emissions irrespective of temperature. Lagoon physicochemical characteristics, such as total Kjeldahl nitrogen (TKN) and total ammoniacal nitrogen (TAN), were highly correlated with emissions (r = 0.52 and 0.55, respectively). Regression models were developed to predict on-farm NH3 emissions and indicated that TKN, TAN, wind speed, air temperature, and pH were the main drivers of these emissions. An on-farm N balance suggested that lagoon NH3-N losses represented 9% of total N lost from the facility, 65% of total lagoon N, and 5% of dairy herd N intake. A process-based model (Integrated Farm System Model) estimated values for N excretion and NH3-N loss from the lagoon within 5% of that measured on-farm. More on-farm research is needed to better refine both process-based models and emission factor estimates to more accurately predict NH3 emissions from lagoons on dairies in the western U.S. Keywords: Ammonia, Emission, Inverse dispersion, Manure.

Methane emissions from dairy lagoons in the western United States.

Methane generation from dairy liquid storage systems is a major source of agricultural greenhouse gas emissions. However, little on-farm research has been conducted to estimate and determine the factors that may affect these emissions. Six lagoons in south-central Idaho were monitored for 1 yr, with CH4 emissions estimated by inverse dispersion modeling. Lagoon characteristics thought to contribute to CH4 emissions were also monitored over this time period. Average emissions from the lagoons ranged from 30 to 126 kg/ha per day or 22 to 517 kg/d. Whereas we found a general trend for greater emissions during the summer, when temperatures were greater, events such as pumping, rainfall, freeze or thaw of lagoon surfaces, and wind significantly increased CH4 emissions irrespective of temperature. Lagoon physicochemical characteristics, such as total solids, chemical oxygen demand, and volatile solids, were highly correlated with emission. Methane prediction models were developed using volatile solids, wind speed, air temperature, and pH as independent variables. The US Environmental Protection Agency methodology for estimating CH4 emissions from manure storage was used for comparison of on-farm CH4 emissions from 1 of the lagoon systems. The US Environmental Protection Agency method underestimated CH4 emissions by 48%. An alternative methodology, using volatile solids degradation factor, provided a more accurate estimate of annual emissions from the lagoon system and may hold promise for applicability across a range of dairy lagoon systems in the United States.

Ammonia emissions from a grazed field estimated by miniDOAS measurements and inverse dispersion modelling

Abstract. Ammonia (NH3) fluxes were estimated from a field being grazed by dairy cattle during spring by applying a backward Lagrangian stochastic model (bLS) model combined with horizontal concentration gradients measured across the field. Continuous concentration measurements at field boundaries were made by open-path miniDOAS (differential optical absorption spectroscopy) instruments while the cattle were present and for 6 subsequent days. The deposition of emitted NH3 to clean patches on the field was also simulated, allowing both net and gross emission estimates, where the dry deposition velocity (vd) was predicted by a canopy resistance (Rc) model developed from local NH3 flux and meteorological measurements. Estimated emissions peaked during grazing and decreased after the cattle had left the field, while control on emissions was observed from covariance with temperature, wind speed and humidity and wetness measurements made on the field, revealing a diurnal emission profile. Large concentration differences were observed between downwind receptors, due to spatially heterogeneous emission patterns. This was likely caused by uneven cattle distribution and a low grazing density, where hotspots of emissions would arise as the cattle grouped in certain areas, such as around the water trough. The spatial complexity was accounted for by separating the model source area into sub-sections and optimising individual source area coefficients to measured concentrations. The background concentration was the greatest source of uncertainty, and based on a sensitivity/uncertainty analysis the overall uncertainty associated with derived emission factors from this study is at least 30–40 %.Emission factors can be expressed as 6 ± 2 g NH3 cow−1 day−1, or 9 ± 3 % of excreted urine-N emitted as NH3, when deposition is not simulated and 7 ± 2 g NH3 cow−1 day−1, or 10 ± 3 % of excreted urine-N emitted as NH3, when deposition is included in the gross emission model. The results suggest that around 14 ± 4 % of emitted NH3 was deposited to patches within the field that were not affected by urine or dung.

Application of inverse dispersion modelling for the determination of PM emission factors from fugitive dust sources in open-pit lignite mines

The operation of large open-pit lignite mines represents a significant source of fugitive dust emissions connected to energy production. Emission inventories can be used to provide operational estimates of the total dust burden in the surrounding areas attributable to mining operations. In this work, a methodology based on the inverse dispersion modelling approach, combining two different dispersion models, is used for preparing a dust emissions inventory for several activity types in the lignite mines of Western Macedonia, Greece. A three-year campaign of field measurement experiments provides the necessary meteorological data and upwind-downwind concentration levels in the area of each activity. A comparison of calculated emission rates provided by the two dispersion models indicates a very good agreement, while the normalised downwind concentration timeseries are accurately reproduced. Emission factors are calculated for each experiment and per-activity, leading to the formulation of empirical relations for the total fugitive dust emissions.

Application of inverse dispersion modelling for the determination of PM emission factors from fugitive dust sources in open-pit lignite mines

Application of inverse dispersion modelling for the determination of PM emission factors from fugitive dust sources in open-pit lignite mines

Parameter identification for continuous point emission source based on Tikhonov regularization method coupled with particle swarm optimization algorithm

In order to identify the parameters of hazardous gas emission source in atmosphere with less previous information and reliable probability estimation, a hybrid algorithm coupling Tikhonov regularization with particle swarm optimization (PSO) was proposed. When the source location is known, the source strength can be estimated successfully by common Tikhonov regularization method, but it is invalid when the information about both source strength and location is absent. Therefore, a hybrid method combining linear Tikhonov regularization and PSO algorithm was designed. With this method, the nonlinear inverse dispersion model was transformed to a linear form under some assumptions, and the source parameters including source strength and location were identified simultaneously by linear Tikhonov-PSO regularization method. The regularization parameters were selected by L-curve method. The estimation results with different regularization matrixes showed that the confidence interval with high-order regularization matrix is narrower than that with zero-order regularization matrix. But the estimation results of different source parameters are close to each other with different regularization matrixes. A nonlinear Tikhonov-PSO hybrid regularization was also designed with primary nonlinear dispersion model to estimate the source parameters. The comparison results of simulation and experiment case showed that the linear Tikhonov–PSO method with transformed linear inverse model has higher computation efficiency than nonlinear Tikhonov-PSO method. The confidence intervals from linear Tikhonov-PSO are more reasonable than that from nonlinear method. The estimation results from linear Tikhonov-PSO method are similar to that from single PSO algorithm, and a reasonable confidence interval with some probability levels can be additionally given by Tikhonov-PSO method. Therefore, the presented linear Tikhonov-PSO regularization method is a good potential method for hazardous emission source parameters identification.

Ultrafine-Particle Emission Factors as a Function of Vehicle Mode of Operation for LDVs Based on Near-Roadway Monitoring.

This paper presents ultrafine-particle (UFP) emission factors (EFs) as a function of vehicle mode of operation (free flow and congestion) using (1) concurrent 5 min measurements of UFPs and carbon monoxide (CO) concentration, wind speed and direction, traffic volume and speed near a roadway that is restricted to light-duty vehicles (LDVs) and (2) inverse dispersion model calculations. Short-term measurements are required to characterize the highly variable and rapidly changing UFP concentration generated by vehicles. Under congestion conditions, the UFP vehicle EFs increased from 0.5 × 10(13) to 2 × 10(13) (particles km(-1) vehicle(-1)) when vehicle flow increased from 5500 to 7500 vehicles/h. For free-flow conditions, the EF is constant at 1.5 × 10(13) (particles km(-1) vehicle(-1)). The analysis is based on the assumption that air-quality models adequately describe the dilution process due to both traffic and atmospheric turbulence. The approach used to verify this assumption was to use an emission factor model to determine EFs for CO and then estimate dilution factors using measured CO concentrations. This procedure eliminates the need to rely only on air quality models to generate dilution factors. The EFs are suitable for fleet emissions under real-world traffic conditions.