Odour impact assessment by means of dynamic olfactometry,dispersion modelling and social participation

This work discusses how it is possible to assess odour impact in presence of multiple similar sources by illustrating a case study. The study was conducted on an area of northern Italy comprising three small municipalities where four rendering plants are located near to each other. Based on the emission data resulting from olfactometric surveys conducted in different periods of the year the overall odour emission rate emitted by each plant were evaluated, showing that the major contributor to the odour impact on the territory was plant 2. These data were linked with meteorological and orographical data in order to evaluate odour dispersion with a model (Calpuff). The results of the odour dispersion modelling confirmed the outcomes of the olfactometric survey and they were further validated through a “questioning” survey, conducted with the aim of involving the population by means of questionnaires for reporting the perceived odour episodes, which showed a good correspondence (86.5%) between odour perceptions and simulated odour immissions.     

Correlating emissions with time and temperature to predict worst-case emissions from open liquid area sources

The two primary factors influencing ambient air pollutant concentrations are emission rate and dispersion rate. Gaussian dispersion modeling studies for odors, and often other air pollutants, vary dispersion rates using hourly meteorological data. However, emission rates are typically held constant, based on one measured value. Using constant emission rates can be especially inaccurate for open liquid area sources, like wastewater treatment plant units, which have greater emissions during warmer weather, when volatilization and biological activity increase. If emission rates for a wastewater odor study are measured on a cooler day and input directly into a dispersion model as constant values, odor impact will likely be underestimated. Unfortunately, because of project schedules, not all emissions sampling from open liquid area sources can be conducted under worst-case summertime conditions. To address this problem, this paper presents a method of varying emission rates based on temperature and time of the day to predict worst-case emissions. Emissions are varied as a linear function of temperature, according to Henry's law, and a tenth order polynomial function of time. Equation coefficients are developed for a specific area source using concentration and temperature measurements, captured over a multiday period using a data-logging monitor. As a test case, time/temperature concentration correlation coefficients were estimated from field measurements of hydrogen sulfide (H2S) at the Rowlett Creek Wastewater Treatment Plant in Garland, TX. The correlations were then used to scale a flux chamber emission rate measurement according to hourly readings of time and temperature, to create an hourly emission rate file for input to the dispersion model ISCST3. ISCST3 was then used to predict hourly atmospheric concentrations of H2S. With emission rates varying hourly, ISCST3 predicted 384 acres of odor impact, compared with 103 acres for constant emissions. Because field sampling had been conducted on relatively cool days (85-90 degrees F), the constant emission rate underestimated odor impact significantly (by 73%).     

Air Pollution Problems of the Steel Industry

Urban air pollution has traditionally been modeled using annual, or at best, seasonal emissions inventories and climatology. These averaging techniques may introduce uncertainty into the analysis, if specific emissions (e.g. SO₂) are correlated with dispersion factors on a short-term basis. This may well be the case for space heating emissions. An analysis of this problem, using hourly climatological and residential emission estimates for six U.S. cities, indicates that the errors introduced using such averages are modest (~ ± 12%) for annual average concentrations. Maximum hourly concentrations vary considerably more, since maximum heat demand and worst case dispersion are in general not coincident. The paper thus provides a basis for estimating more realistic air pollution Impacts due to residential space heating.     

Application of a combined measurement and modeling method to quantify windblown dust emissions from the exposed playa at Mono Lake, California

Particulate matter < or =10 microm (PM10) emissions due to wind erosion can vary dramatically with changing surface conditions. Crust formation, mechanical disturbance, soil texture, moisture, and chemical content of the soil can affect the amount of dust emitted during a wind event. A refined method of quantifying windblown dust emissions was applied at Mono Lake, CA, to account for changing surface conditions. This method used a combination of real-time sand flux monitoring, ambient PM10 monitoring, and dispersion modeling to estimate dust emissions and their downwind impact. The method identified periods with high emissions and periods when the surface was stable (no sand flux), even though winds may have been high. A network of 25 Cox sand catchers (CSCs) was used to measure the mass of saltating particles to estimate sand flux rates across a 2-km2 area. Two electronic sensors (Sensits) were used to time-resolve the CSC sand mass to estimate hourly sand flux rates, and a perimeter tapered element oscillating microbalance (TEOM) monitor measured hourly PM10 concentrations. Hourly sand flux rates were related by dispersion modeling to hourly PM10 concentrations to back-calculate the ratio of vertical PM10 flux to horizontal sand flux (K-factors). Geometric mean K-factor values (K(f)) were found to change seasonally, ranging from 1.3 x 10(-5) to 5.1 x 10(-5) for sand flux measured at 15 cm above the surface (q15). Hourly PM10 emissions, F, were calculated by applying seasonal K-factors to sand flux measurements (F = K(f) x q15). The maximum hourly PM10 emission rate from the study area was 76 g/m2 x hr (10-m wind speed = 23.5 m/sec). Maximum daily PM10 emissions were estimated at 450 g/m2 x day, and annual emissions at 1095 g/m2 x yr. Hourly PM10 emissions were used by the U.S. Environmental Protection Agency (EPA) guideline AERMOD dispersion model to estimate downwind ambient impacts. Model predictions compared well with monitor concentrations, with hourly PM10 ranging from 16 to over 60,000 microg/m3 (slope = 0.89, R2 = 0.77).     

Unsteady characteristics of the dispersion process in the vicinity of a pig barn. Wind tunnel experiments and comparison with field data

Two complementary methods, field experiments and physical modelling in a wind tunnel, have been used to investigate the dispersion of tracer-gas released from the ventilation system of a pig barn, under near-neutral stability conditions. In both cases, concentration fluctuations were measured and the deduced statistical results were compared. The choice of data processing applied to the time series of concentration was motivated by special issues in the assessment of odour annoyances: “where, how often, how long and how strong does it smell?” These features were described by the mean concentration distribution, the intermittency factor, the persistence and the 90-percentile. The good agreement between field and wind tunnel data confirmed the ability to replicate in wind tunnel the unsteady properties of a dispersion process, if the unsteady turbulent behaviour of the atmospheric boundary layer was properly modelled.A parametrical study of the influence on the dispersion process of the ratio between the exhaust velocity from the stack and the wind speed was then performed in wind tunnel. The fundamental outcome was that the near-field dispersion process under neutral stability conditions, despite the strong influence of the building wake, was for the most part driven by the meandering behaviour of the plume and not so much by the diffusion process.This study was also focused on the influence of the averaging time on the statistical results. The scatter generated by using dimensionless averaging times 200<T_a^*<400 (used during field experiments) instead of T_a^*→∞ (averaging time to ensure reproducible statistic results) was quantified in the wind tunnel. A degree of representativity of the results obtained from short-term samples, compared to fully converged statistical results was therefore assessed.     

Correlating Emissions with Time and Temperature to Predict Worst-Case Emissions from Open Liquid Area Sources

Abstract

The two primary factors influencing ambient air pollutant concentrations are emission rate and dispersion rate. Gaussian dispersion modeling studies for odors, and often other air pollutants, vary dispersion rates using hourly meteorological data. However, emission rates are typically held constant, based on one measured value. Using constant emission rates can be especially inaccurate for open liquid area sources, like wastewater treatment plant units, which have greater emissions during warmer weather, when volatilization and biological activity increase. If emission rates for a wastewater odor study are measured on a cooler day and input directly into a dispersion model as constant values, odor impact will likely be underestimated. Unfortunately, because of project schedules, not all emissions sampling from open liquid area sources can be conducted under worst-case summertime conditions. To address this problem, this paper presents a method of varying emission rates based on temperature and time of the day to predict worst-case emissions. Emissions are varied as a linear function of temperature, according to Henry’s law, and a tenth order polynomial function of time. Equation coefficients are developed for a specific area source using concentration and temperature measurements, captured over a multiday period using a data-logging monitor. As a test case, time/temperature concentration correlation coefficients were estimated from field measurements of hydrogen sulfide (H₂S) at the Rowlett Creek Wastewater Treatment Plant in Garland, TX. The correlations were then used to scale a flux chamber emission rate measurement according to hourly readings of time and temperature, to create an hourly emission rate file for input to the dispersion model ISCST3. ISCST3 was then used to predict hourly atmospheric concentrations of H₂S. With emission rates varying hourly, ISCST3 predicted 384 acres of odor impact, compared with 103 acres for constant emissions. Because field sampling had been conducted on relatively cool days (85–90 °F), the constant emission rate underestimated odor impact significantly (by 73%).     

(M)VOC and composting facilities. Part 2: (M)VOC dispersal in the environment

BACKGROUND, AIMS AND SCOPE: Composting facilities are known to release odorous volatiles due to biodegradation of municipal waste and plant residues. Although odour perception and its grading is influenced by experience, attitude and adaptation, these emissions have created a lack of acceptance for residents in the vicinity of composting facilities. Enclosure of compost pile halls, ventilation systems and biofilters are often insufficient to minimise the burden of compost-derived compounds in the air. Moreover, economic considerations forced smaller communities to establish less sophisticated facilities with open storage areas and other relevant sources for wind-borne dispersal of bioaerosols. Aim of the present study was to characterise the immission and dispersal of microbial volatiles (MVOC) and, besides, to find coincidences between MVOC and compost odour. METHODS: In the course of this study, the surroundings of two composting facilities, differing in their type of process engineering, were investigated for emission of volatiles in the environment. Both microbially and plant-derived substances were assessed, several of which have low odour thresholds. Air samples were taken in distances ranging from 50 to 800 m in a downwind direction from each facility. RESULTS AND DISCUSSION: Compost-derived and microbial volatile organic compounds (MVOC) were found at distances of up to 800 m from the composting facilities. Terpenes like alpha-pinene, camphene and camphor were the dominant compounds and coincided with typical compost odour, whereas several typical MVOC were not found at greater distances. The terpenes in combination with certain MVOC may play an important role in the perception of compost odour. Exposure concentrations were not of toxicological relevance, but sensory irritation and psychohygienic effects due to an annoyance potential of such compounds should not be dismissed. RECOMMENDATIONS AND OUTLOOK: Although terpenes are generally associated with pleasant odour characteristics, they seemed to contribute to malodours in a mixture with other VOC, in this context of volatile waste from compost facilities. Malodorous emissions from biowaste have to be considered as sources of health complaints and the investigation of mixtures of compost-derived volatiles is still inevitable. Exposure levels have to be discussed taking VOC mixtures into account. Within composting facilities, technical devices have to be improved to minimise dispersal of volatiles to prevent residents from immissions eventually causing health complaints.     

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.     

Emissions Variability Processor (EMVAP): Design,evaluation, and application

Emissions of pollutants such as SO₂ and NOx from external combustion sources can vary widely depending on fuel sulfur content, load, and transient conditions such as startup, shutdown, and maintenance/malfunction. While monitoring will automatically reflect variability from both emissions and meteorological influences, dispersion modeling has been typically conducted with a single constant peak emission rate. To respond to the need to account for emissions variability in addressing probabilistic 1-hr ambient air quality standards for SO₂ and NO₂, we have developed a statistical technique, the Emissions Variability Processor (EMVAP), which can account for emissions variability in dispersion modeling through Monte Carlo sampling from a specified frequency distribution of emission rates. Based upon initial AERMOD modeling of from 1 to 5 years of actual meteorological conditions, EMVAP is used as a postprocessor to AERMOD to simulate hundreds or even thousands of years of concentration predictions. This procedure uses emissions varied hourly with a Monte Carlo sampling process that is based upon the user-specified emissions distribution, from which a probabilistic estimate can be obtained of the controlling concentration. EMVAP can also accommodate an advanced Tier 2 NO₂ modeling technique that uses a varying ambient ratio method approach to determine the fraction of total oxides of nitrogen that are in the form of nitrogen dioxide. For the case of the 1-hr National Ambient Air Quality Standards (NAAQS, established for SO₂ and NO₂), a “critical value” can be defined as the highest hourly emission rate that would be simulated to satisfy the standard using air dispersion models assuming constant emissions throughout the simulation. The critical value can be used as the starting point for a procedure like EMVAP that evaluates the impact of emissions variability and uses this information to determine an appropriate value to use for a longer term (e.g., 30-day) average emission rate that would still provide protection for the NAAQS under consideration. This paper reports on the design of EMVAP and its evaluation on several field databases that demonstrate that EMVAP produces a suitably modest overestimation of design concentrations. We also provide an example of an EMVAP application that involves a case in which a new emission limitation needs to be considered for a hypothetical emission unit that has infrequent higher-than-normal SO₂ emissions.

ImplicationsEmissions of pollutants from combustion sources can vary widely depending on fuel sulfur content, load, and transient conditions such as startup and shutdown. While monitoring will automatically reflect this variability on measured concentrations, dispersion modeling is typically conducted with a single peak emission rate assumed to occur continuously. To realistically account for emissions variability in addressing probabilistic 1-hr ambient air quality standards for SO₂ and NO₂, the authors have developed a statistical technique, the Emissions Variability Processor (EMVAP), which can account for emissions variability in dispersion modeling through Monte Carlo sampling from a specified frequency distribution of emission rates.     

Estimating Emissions from Air Concentration Measurements

A one-year-long experiment in which two different tracers were simultaneously released from two different locations was used to test various hybrid receptor modeling techniques to estimate the tracer emissions using the measured air concentrations and a meteorological model. Air concentrations were measured over an 8-hour averaging time at three sites 14 to 40 km downwind. When the model was used to estimate emissions at only one tracer source, 6 percent of the short-term (8-h) emission estimates were within a factor of 2 of the actual emissions. Temporal averaging of the 8-h data enhanced the precision of the estimate such that after 10 days 42 percent of the estimates were within a factor of 2 and after six months all of them (each source-receptor pair) were within a factor of 2. To test the ability of the model to separate two sources, both tracer sources were combined, and a multiple linear regression technique was used to determine the emissions from each source from a time series of air concentration measurements representing the sum of both tracers. In general, 50 percent of the short-term estimates were within a factor of 10, 25 percent were biased low, and in another 25 percent the regression technique failed. The bias and failures are attributed to low or no correlation between measured air concentrations and model calculated dispersion factors. In the regression method increased temporal averaging did not consistently improve the emission estimate since the ability of the model to distinguish emissions between sources was diminished with increased averaging time. However, including progressively longer time periods (more data) into the regression or spatially averaging the data over all the receptors was found to be the most effective method to improve the estimated emissions. At best about 75 percent of the estimated monthly emission data were within a factor of 10 of the measured values. This suggests that the usefulness of meteorological models and statistical methods to address questions of source attribution requires many data points to reduce the uncertainty in the emission estimates.     

Identification of odour sources in an industrial park from resident diaries statistics

A methodology based on social participation through the use of resident diaries was applied to evaluate the odour annoyance in the surroundings of an industrial park in Belgium during one year. The studied area covers about 8 km² and includes13 potential odour emitting facilities. The network involved 44 residents in the survey, among whom 19 were particularly considered for a detailed analysis. The questionnaire aimed at providing an odour rating twice-daily on a 6-level scale together with an odour type.The fact that the response rate corresponding to “no-odour” was high (79%) is particularly discussed. Some tests are proposed to check the plausibility of the answers, the coherence within clusters of residents and the individual performance of respondents to discriminate among odour ratings. The odour rose is presented as an attractive and visual tool, particularly suited in the case of multi-source areas, to map the different odour emissions, to point out the most worrying ones, to identify others creating less annoyance and possibly new unpredicted ones. The resident diary method has proven to be particularly useful, conjointly to other ones, to the case of multi-sources facilities in large areas, when the purpose is the assessment of the long-term evolution of odour annoyance.     

Adherence of Sulfur Dioxide Concentrations in the Vicinity of a Steam Plant to Plume Dispersion Models

Air monitoring data for a calendar year at one of the TVA power plants has been used to evaluate the appropriateness of the Sutton, the Bosanquet and Pearson, and the USPHS-TVA atmospheric dispersion models to predict ground level concentrations of sulfur dioxide from emission and meterological data. Aerometric data included one half hourly average sulfur dioxide concentrations, recorded by four Thomas autometers, and the necessary meterological parameters for the solving of atmospheric dispersion models. Based on these meterological parameters and observed plume rise data, over 4000 one half hourly average maximum and minimum expected ground line sulfur dioxide concentrations were predicted for each of the above dispersion models by the use of computer techniques. The plant is a line source; however, an empirical correction was applied to emission data to reduce them to emissions for an equivalent point source. The predicted sulfur dioxide levels for each of the dispersion models were compared to the measured levels throughout the year. Three different sets of diffusion coefficients were applied to the Sutton model and successful predictions, according to a criterion utilizing an acceptable range of concentration, varied from 66 to 93%. The Bosanquet and Pearson model produced successful predictions 90% of the time, while the USPHS-TVA model was successful 94% of the time.Unsuccessful predictions were primarily overestimates.     

Effective pollutant emission heights for atmospheric transport modelling based on real-world information

Emission data needed as input for the operation of atmospheric models should not only be spatially and temporally resolved. Another important feature is the effective emission height which significantly influences modelled concentration values. Unfortunately this information, which is especially relevant for large point sources, is usually not available and simple assumptions are often used in atmospheric models. As a contribution to improve knowledge on emission heights this paper provides typical default values for the driving parameters stack height and flue gas temperature, velocity and flow rate for different industrial sources. The results were derived from an analysis of the probably most comprehensive database of real-world stack information existing in Europe based on German industrial data. A bottom-up calculation of effective emission heights applying equations used for Gaussian dispersion models shows significant differences depending on source and air pollutant and compared to approaches currently used for atmospheric transport modelling.     

Comparison of emission rate values for odour and odorous chemicals derived from two sampling devices

Field and laboratory measurements identified a complex relationship between odour emission rates provided by the US EPA dynamic emission chamber and the University of New South Wales wind tunnel. Using a range of model compounds in an aqueous odour source, we demonstrate that emission rates derived from the wind tunnel and flux chamber are a function of the solubility of the materials being emitted, the concentrations of the materials within the liquid; and the aerodynamic conditions within the device – either velocity in the wind tunnel, or flushing rate for the flux chamber. The ratio of wind tunnel to flux chamber odour emission rates (OU m^?2 s) ranged from about 60:1 to 112:1. The emission rates of the model odorants varied from about 40:1 to over 600:1.These results may provide, for the first time, a basis for the development of a model allowing an odour emission rate derived from either device to be used for odour dispersion modelling.     

Comparison of a Gaussian diffusion model with guidelines for calculating the separation distance between livestock farming and residential areas to avoid odour annoyance

Complaints by the neighbourhood due to odour pollution from livestock farming are increasing. Therefore, some countries have already developed guidelines to address odour from livestock. These guidelines are in use to assess the necessary separation distance between livestock buildings and residential areas such that odour is not felt as an annoyance. In all these guidelines, the separation distance is calculated as a function of the rate of pollution. These are mainly power functions with an exponent between 0.3 and 0.5. The Austrian regulatory dispersion model, a Gauss model, is used to calculate the frequency distribution of the dilution factor for 12 classes of distances between 50 and 500 m downwind from the source. These data were fitted to an extended Weibull distribution of the dilution factor to determine the exponent of the power function describing the separation distance as a function of the emission. The exponent has a value of about 0.72. This result, achieved with a wind and stability statistics representative for the Austrian flatlands north of the Alps, indicates a stronger dependance of the separation distance from the odour emission than suggested by the guidelines.     

Modelling local and synoptic scale influences on ozone concentrations in a topographically complex region of Southern Italy

A modelling study with the on-line coupled Eulerian chemical-weather model WRF/Chem for the Southern Italian region around Cosenza (Calabria) was conducted to identify the influences of synoptic scale meteorology, local scale wind systems and local emissions on ozone concentrations in this orographically complex region. Four periods of 5–7 days were chosen, one from each season, which had wind pattern characteristics representative of typical local climatological conditions, in order to study the local versus non-local impacts on ozone transport and formation. To account for the complex terrain, the horizontal resolution of the smallest modelling domain was 3 km. Model results were compared with measurements to demonstrate the capability of the model to reproduce ozone concentrations in the region. The comparison was favourable with a mean bias of ?1.1 ppb. The importance of local emissions on ozone formation and destruction was identified with the use of three different emission scenarios. Generally the influence of regional emissions on the average ozone concentration was small. However during periods when mountain-sea wind systems were well developed and synoptic scale winds were weak, the influence of local emissions from the urban area was at its greatest. The maximum influence of local emissions on ozone concentrations was 18 ppb.     

Quantification of hourly variability in NO(x) emissions for baseload coal-fired power plants

The objectives of this paper are to (1) quantify variability in hourly utility oxides of nitrogen (NO(x)) emission factors, activity factors, and total emissions; (2) investigate the autocorrelation structure and evaluate cyclic effects at short and long scales of the time series of total hourly emissions; (3) compare emissions for the ozone (O3) season versus the entire year to identify seasonal differences, if any; and (4) evaluate interannual variability. Continuous emissions monitoring data were analyzed for 1995 and 1998 for 32 units from nine baseload power plants in the Charlotte, NC, airshed. Unit emissions have a strong 24-hr cycle attributable primarily to the capacity factor. Typical ranges of the coefficient of variation for emissions at a given hour of the day were from 0.2 to 0.45. Little difference was found when comparing weekend emissions with the entire week or when comparing the O3 season with the entire year. There were substantial differences in the mean and standard deviation of emissions when comparing 1995 and 1998 data, indicative of the effect of retrofits of control technology during the intervening time. The wide range of variability and its autocorrelation should be accounted for when developing probabilistic utility emission inventories for analysis of near-term future episodes.     

Intra- and inter-annual variability of VOC emissions from natural and semi-natural vegetation in Europe and neighbouring countries

Biogenic VOC emission estimates from the earth's surface are crucial input parameters in air quality models. Knowledge accumulated in the last years about BVOC source distributions and chemical compound species emission profiles in Europe as well as the demand of air quality modellers for a finer resolution in space and time of BVOC estimates have led to the set-up of new emission modelling systems. An updated fast BVOC emission modelling platform explicitly considering the seasonality of emission potentials and leaf temperature gradients in forest canopies by the semi-empirical emission module (seBVOC) will be proposed and used for estimating hourly values of chemical compound-specific emissions in Europe (33–68° north; 10° west to 40° east) in the years 1997, 2000, 2001, and 2003. Spatial resolution will be 10 km by 10 km. The database used contains latest land and forest distributions, updated foliar biomass densities, leaf area indices (LAI), and plant as well as chemical compound-specific emission potentials, if available. Meteorological input parameters for the respective years will be generated using the non-hydrostatic meteorological model MM5. Highest BVOC emissions occur in daytime hours around noon from the end of May to mid-August in the Mediterranean area and from the mid of June to the end of July in the boreal forests. Comparison of 3 BVOC model approaches will reveal that for July 2003, the European isoprene and monoterpene totals range from 1124 Gg to 1446 Gg and from 338 Gg to 1112 Gg, respectively. Small-scale deviations may be as high as ±0.6 Mg km^?2 for July 2003, reflecting the current uncertainty range for BVOC estimates. Key sources of errors in inventories are still insufficiently detailed land use data for some areas and lacking chemically speciated plant-specific emission potentials in particular in boreal, south-eastern, and northern African landscapes. The hourly emissions of isoprene, speciated terpenes, and oxyVOC have been made available by the NatAir database.     

Quantification of Hourly Variability in NOx Emissions for Baseload Coal-Fired Power Plants

Abstract

The objectives of this paper are to (1) quantify variability in hourly utility oxides of nitrogen (NO_x) emission factors, activity factors, and total emissions; (2) investigate the autocorrelation structure and evaluate cyclic effects at short and long scales of the time series of total hourly emissions; (3) compare emissions for the ozone (O₃) season versus the entire year to identify seasonal differences, if any; and (4) evaluate interannual variability. Continuous emissions monitoring data were analyzed for 1995 and 1998 for 32 units from nine baseload power plants in the Charlotte, NC, airshed. Unit emissions have a strong 24-hr cycle attributable primarily to the capacity factor. Typical ranges of the coefficient of variation for emissions at a given hour of the day were from 0.2 to 0.45. Little difference was found when comparing weekend emissions with the entire week or when comparing the O₃ season with the entire year. There were substantial differences in the mean and standard deviation of emissions when comparing 1995 and 1998 data, indicative of the effect of retrofits of control technology during the intervening time. The wide range of variability and its autocorrelation should be accounted for when developing probabilistic utility emission inventories for analysis of near-term future episodes.     

Odorous emission reduction from a waste landfill with an optimal protection system based on fuzzy logic

Effective landfill management and operation require an accurate evaluation of the occurrence and extent of odour emission events, which are among the main causes of resident complaints and concerns, in particular in densely urbanised areas. This paper proposes a fuzzy optimal protection system (FOPS) based on fuzzy logic to manage odour production from a municipal solid waste (MSW) landfill. The case study is a MSW landfill in an old quarry site located 6 km north-west of Naples city centre (Italy). The aim is to reduce the odour nuisance in the area surrounding the landfill where there are several sensitive receptors. FOPS is based on logical relationships between local atmospheric dynamics, number and intensity of odour pollution events detected by certain fixed receptors and odour emission rate via an optimal fuzzy approach. Such system allows to start, in real time, established mitigation actions required to further reduce the odorous emissions from the landfill itself (e.g. spraying of perfumed substances and activation of extraction wells), especially when weather conditions might not be favourable and cause by causing a higher odour perception. The fuzzy system was coupled with the air pollutant transport software CALPUFF to simulate the odour dispersion in the considered area taking into account both different odour emission rates and local weather conditions. FOPS results show that this approach can be very useful as, by measuring the odour concentrations, a significant reduction of the odour exceedances over the thresholds fixed, to minimise the olfactory harassment, was observed in the whole area studied.