Source extraction information from air quality data monitored in an Argentinean steel mill

A statistical analysis of a series of ambient air concentrations of suspended particulate matter (SPM) and NO2 is presented. Measurements were taken at four sites that belong to an Argentinean steel mill and in another site located in its vicinity. The air pollutants were measured during a three-week exploratory sampling. The monitoring sites were selected on the basis of relevant characteristics of the emission sources and the corresponding climatological statistics of the last decade. Suspended particulate matter with aerodynamic diameter of less than 10 microm (PM10) and NO2 were continuously measured at only one site, while 1-hr samples of NO2 and 24-hr samples of total SPM and SO2 were collected at the other sites. The registered concentrations show that SPM was the pollutant of major concern. A first estimate about the nature of the contribution of the different sources of particles and NO2 present in the area was obtained through the statistical analysis of measured concentration data coupled with prevalent meteorological variables.     

A Simulation Model for Air Pollution over Connecticut

A different approach to mathematically modeling large-scale atmospheric processes is presented. Whereas past approaches have been to develop a model based on an accumulation of information from a specific geographical area, resulting in a model applicable to that area only, we have developed a general mathematical model applicable to any geographical area. The model’s applicability is controlled by specifying the input information describing the meteorological situation and pollution source configuration. A rectangular array of grid points is used to specify both the wind field, by using stream functions, and the average source strength of some pollutant for the area represented by the grid. The diffusion problem is divided into two areas: transport by the mean wind field, and dispersion based on travel time and distance as described by empirical equations. Trajectories of pollutants are traced backwards from the points of interest in the course of the calculations and the contributions of all sources that affect the points of interest are accumulated. The model requires an array of source strength information. An inventory of pollution sources in the State of Connecticut was compiled and maps of source strengths were prepared for five pollutants on a 5000-ft grid-square array. Maps of sulfur dioxide and carbon monoxide source strengths are presented with the resulting concentration distribution for “typical” meteorological conditions. The model permits the changing of meteorological or source values at predetermined intervals so that diurnal changes are incorporated in the calculations. The model has not been verified, but the values of pollution concentration are the right order of magnitude and the resulting patterns are as expected.     

Estimating Future NO2 Levels in the Greater Los Angeles Area by Source–Type Contribution

Two indicator pollutants, carbon monoxide (CO) for mobile source influence and sulfur dioxide (SO₂) for stationary source influence, were used to estimate source-type contributions to ambient NO₂ levels in a base year and to predict NO₂ concentrations in a future year. For a specific source-receptor pair, the so-called influence coefficient of each of three source categories (mobile sources, power plants, and other stationary sources) was determined empirically from concurrent measurements of CO and SO₂ concentrations at the receptor site and CO and SO₂ emissions from each source category in the source area. Those coefficients, which are considered time invariant, were used in conjunction with the base year and future year NO _x emission values to estimate source-type contribution to ambient NO₂ levels at seven study sites selected from the Greater Los Angeles area for both the base year period, 1974 through 1976, and the future goal year of 1987 in which the air quality standards for NO₂ are to be attained. The estimated NO₂ air quality at the seven sites is found to meet the national annual standard of 5 pphm and over 99.9% of total hours, the California 1-hr NO₂ standard of 25 pphm in 1987. The estimated power plant contributions to ambient NO₂ levels are found to be considerably smaller than those to total NO _x emissions in the area. Providing that reasonably complete air quality and emissions data are available, the present analysis method may prove to be a useful tool in evaluating source contributions to both short-term peak and long-term average NO₂ concentrations for use in control strategy development.     

Source apportionment of ambient non-methane hydrocarbons in Hong Kong: application of a principal component analysis/absolute principal component scores (PCA/APCS) receptor model

Receptor-oriented source apportionment models are often used to identify sources of ambient air pollutants and to estimate source contributions to air pollutant concentrations. In this study, a PCA/APCS model was applied to the data on non-methane hydrocarbons (NMHCs) measured from January to December 2001 at two sampling sites: Tsuen Wan (TW) and Central & Western (CW) Toxic Air Pollutants Monitoring Stations in Hong Kong. This multivariate method enables the identification of major air pollution sources along with the quantitative apportionment of each source to pollutant species. The PCA analysis identified four major pollution sources at TW site and five major sources at CW site. The extracted pollution sources included vehicular internal engine combustion with unburned fuel emissions, use of solvent particularly paints, liquefied petroleum gas (LPG) or natural gas leakage, and industrial, commercial and domestic sources such as solvents, decoration, fuel combustion, chemical factories and power plants. The results of APCS receptor model indicated that 39% and 48% of the total NMHCs mass concentrations measured at CW and TW were originated from vehicle emissions, respectively. 32% and 36.4% of the total NMHCs were emitted from the use of solvent and 11% and 19.4% were apportioned to the LPG or natural gas leakage, respectively. 5.2% and 9% of the total NMHCs mass concentrations were attributed to other industrial, commercial and domestic sources, respectively. It was also found that vehicle emissions and LPG or natural gas leakage were the main sources of C(3)-C(5) alkanes and C(3)-C(5) alkenes while aromatics were predominantly released from paints. Comparison of source contributions to ambient NMHCs at the two sites indicated that the contribution of LPG or natural gas at CW site was almost twice that at TW site. High correlation coefficients (R(2) > 0.8) between the measured and predicted values suggested that the PCA/APCS model was applicable for estimation of sources of NMHCs in ambient air.     

Bootstrap estimates of factor model variability

Receptor models for air pollutants in an airshed are constructed from a set of measured concentrations and used for apportioning sources in a quantitative way. Such a dataset even if large is but a sample from the population of pollutants occurring over time in the airshed. Thus the ensuing receptor model and its parameters constitute but one realization of a range of possible experiments. The problem studied in this paper is how large the inherent ranges of variation of the model parameters are, i.e. how reliable the model is. Factor analysis models, where data are autoscaled prior to analysis, were selected for study.Pseudo-repetitions of the experiment are carried out by bootstrapping of the original data and statistics on the model parameters are compiled and reported. The validity of the results of such numerical experiments are based on the assumption that the original data are really representative since by bootstrapping, new artificial data are generated from this set. A number of screening tests for acceptance of data and models are applied to avoid bias and errors in the results. Means and standard deviations of communalities and factor loadings are computed and compared to the original parameters. Regression analysis is used in a search for functional relationships between standard deviations and model parameters.The general conclusion is that well modelled variables carry little model uncertainty and that the largest standard deviations amounting to about 10% are found for loadings in the mid-range.     

Air quality network design for Saudi Arabia using long-range transport model

The need for air quality surveillance is growing during the third five-year plan in Saudi Arabia. The main emphasis in the initial stage of this plan is to select the sites of air monitoring stations on the basis of scientific principles. To carry out this study, emission inventories of major point sources are compiled. Meteorological data for synoptic and upper air stations within the Kingdom of Saudi Arabia on wind speed, wind direction, temperature and pressure are collected and processed. Air Resources Laboratories (ARL) model is used to calculate long range trajectories of air parcels and to estimate concentration levels at various grid points within the region. Concentration contour maps are then prepared to identify adversely affected zones and potential air monitoring sites.     

Spatial characteristics of fine particulate matter: identifying representative monitoring locations in Seattle, Washington

This study investigates how PM2.5 varies spatially and how these spatial characteristics can be used to identify potential monitoring sites that are most representative of the overall ambient exposures to PM2.5 among susceptible populations in the Seattle, WA, area. Data collected at outdoor sites at the homes of participants of a large exposure assessment study were used in this study. Harvard impactors (HIs) were used at 40 outdoor sites throughout the Seattle metropolitan area. Up to six sites at a time were monitored for 10 consecutive 24-hr average periods. A fixed-effect analysis of variance (ANOVA) model that included date and location effects was used to analyze the spatial variability of outdoor PM2.5 concentrations. Both date and location effects were shown to be highly significant, explaining 92% of the variability in outdoor PM2.5 measurements. The day-to-day variability was 10 times higher than the spatial variability between sites. The site mean square was more than twice the error mean square, showing that differences between sites, while modest, are potentially an important contribution to measurement error. Variances of the model residuals and site effects were examined against spatial characteristics of the monitoring sites. The spatial characteristics included elevation, distance from arterials, and distance from major PM2.5 point sources. Results showed that the most representative PM2.5 sites were located at elevations of 80-120 m above sea level, and at distances of 100-300 m from the nearest arterial road. Location relative to industrial PM2.5 sources is not a significant predictor of residential outdoor PM2.5 measurements. Additionally, for sites to be representative of the average population exposures to PM2.5 among those highly susceptible to the health effects of PM2.5, areas of high elderly population density were considered. These representative spatial characteristics were used as multiple, overlapping criteria in a Geographic Information System (GIS) analysis to determine where the most representative sites are located.     

Apportionment of ambient primary and secondary fine particulate matter during a 2001 summer intensive study at the CMU Supersite and NETL Pittsburgh site

Gaseous and particulate pollutant concentrations associated with five samples per day collected during a July 2001 summer intensive study at the Pittsburgh Carnegie Mellon University (CMU) Supersite were used to apportion fine particulate matter (PM2.5) into primary and secondary contributions using PMF2. Input to the PMF2 analysis included the concentrations of PM2.5 nonvolatile and semivolatile organic material, elemental carbon (EC), ammonium sulfate, trace element components, gas-phase organic material, and NO(x), NO2, and O3 concentrations. A total of 10 factors were identified. These factors are associated with emissions from various sources and facilities including crustal material, gasoline combustion, diesel combustion, and three nearby sources high in trace metals. In addition, four secondary sources were identified, three of which were associated with secondary products of local emissions and were dominated by organic material and one of which was dominated by secondary ammonium sulfate transported to the CMU site from the west and southwest. The three largest contributors to PM2.5 were secondary transported material (dominated by ammonium sulfate) from the west and southwest (49%), secondary material formed during midday photochemical processes (24%), and gasoline combustion emissions (11%). The other seven sources accounted for the remaining 16% of the PM2.5. Results obtained at the CMU site were comparable to results previously reported at the National Energy Technology Laboratory (NETL), located approximately 18 km south of downtown Pittsburgh. The major contributor at both sites was material transported from the west and southwest. Some difference in nearby sources could be attributed to meteorology as evaluated by HYSPLIT model back-trajectory calculations. These findings are consistent with the majority of the secondary ammonium sulfate in the Pittsburgh area being the result of contributions from distant transport, and thus decoupled from local activity involving organic pollutants in the metropolitan area. In contrast, the major local secondary sources were dominated by organic material.     

Air pollution and the influence of sources on Paulinia (Brazil) and surroundings

The air quality in the industrial area and surroundings of the city of Paulinia (state of Sao Paulo, Brazil) has been investigated by analysing the concentration of air pollutants (SO₂, PM₁₀, NO, NO₂, CO and ozone) and identifying the main sources of air pollution. A mobile pollutant monitoring unit was used to collect the data at five different sites from November 2000 to July 2002. Critical pollutants were determined based on air quality standards, and sources were identified by principal component analysis. Photochemical reactions play an important role in Paulinia's air pollution: three out of five monitored sites showed levels exceeding the standard air quality of ozone. SO₂ and PM₁₀ appeared as pollutants deserving special attention. All the monitored sites showed vehicles and industrial plants (which release SO₂) to be significant sources of pollution. Depending on the location, ozone was related mainly with vehicular or industrial sources.     

粤西某市小容量河流水污染综合控制研究

对粤西某石化城河段几种主要污染物进行了容量计算 ,发现即使工业污染源全部达标 ,其水质状况与目标的差距仍较大 ,据此提出小容量河流的观点。为保护水环境 ,结合流域实际情况 ,本文提出改变以往单纯依靠工业污染源达标控制手段 ,而采用多手段相结合的综合管理模式来改善水环境的观点 ,并以中长期水环境规划年内目标值为依据 ,分析了本方法的有效性、经济性和可操作性 ,由此对其他具相似条件小容量河流的城市水环境管理起到借鉴性作用。     

On-road traffic emissions in a megacity

A new annual bottom–up emission inventory of criteria pollutants and greenhouse gases from on-road mobile sources was developed for 2006 for the metropolitan area of Buenos Aires, Argentina, within a four-year regional project aimed at providing tools for chemical weather forecast in South America. Under the scarcity of local emission factors, we collected data from measuring campaigns performed in Argentina, Brazil, Chile and Colombia and compiled a data set of regional emission factors representative of Latin American fleets and driving conditions. The estimated emissions were validated with respect to downscaled national estimates and the EDGAR global emission database. Our results highlight the role of older technologies accounting in average for almost 80% of the emissions of all species. The area exhibits higher specific emissions than developed countries, with figures two times higher for criteria pollutants. We analyzed the effect on emissions of replacing gasoline by compressed natural gas, occurring in Argentina since 1995. We identified (i) a relationship between number of vehicles and a compound socioeconomic indicator, and (ii) time-lags in vehicle technologies between developed and developing countries, which can be respectively applied for spatial disaggregation and the development of projections for other Latin American cities. The results may also be employed to complement global emission inventories and by local policy makers as an environmental management tool.     

An Efficient Gaussian-Plume Multiple-Source Air Quality Algorithm

The information presented in this paper is directed to air pollution scientists with an interest in applying air quality simulation models. RAM is the three letter designation for this efficient Gaussian-plume multiple-source air quality algorithm. RAM is a method of estimating short-term dispersion using the Gaussian steady-state model. This algorithm can be used for estimating air quality concentrations of relatively stable pollutants for averaging times from an hour to a day in urban areas from point and area sources. The algorithm is applicable for locations with level or gently rolling terrain where a single wind vector for each hour is a good approximation to the flow over the source area considered. Calculations are performed for each hour. Hourly meteorological data required are wind direction, wind speed, stability class, and mixing height. Emission information required of point sources consists of source coordinates, emission rate, physical height, stack gas volume flow and stack gas temperature. Emission information required of area sources consists of south-west corner coordinates, source area, total area emission rate and effective area source height. Computation time is kept to a minimum by the manner in which concentrations from area sources are estimated using a narrow plume hypothesis and using the area source squares as given rather than breaking down all sources to an area of uniform elements. Options are available to the user to allow use of three different types of receptor locations: 1 ) those whose coordinates are input by the user, 2) those whose coordinates are determined by thé model and are downwind óf significant point and area sources where maxima are likely to occur, and 3) those whose coordinates are determined by the model to give good area coverage of a specific portion of the region. Computation time is also decreased by keeping the number of receptors to a minimum.     

Use of AERMOD to Determine a Hydrogen Sulfide Emission Factor for Swine Operations by Inverse Modeling

This study was conducted to determine both optimal settings applied to the plume dispersion model, AERMOD, and a scalable emission factor for accurately determining the spatial distribution of hydrogen sulfide concentrations in the vicinity of swine concentrated animal feeding operations (CAFOs). These operations emit hydrogen sulfide from both housing structures and waste lagoons. With ambient measurements made at 4 stations within 1 km of large swine CAFOs in Iowa, an inverse-modeling approach applied to AERMOD was used to determine hydrogen sulfide emission rates. CAFO buildings were treated as volume sources whereas nearby lagoons were modeled as area sources. The robust highest concentration (RHC), calculated for both measured and modeled concentrations, was used as the metric for adjusting the emission rate until the ratio of the two RHC levels was unity. Utilizing this approach, an average emission flux rate of 0.57 μg/m(2)-s was determined for swine CAFO lagoons. Using the average total animal weight (kg) of each CAFO, an average emission factor of 6.06 × 10(-7) μg/yr-m(2)-kg was calculated. From studies that measured either building or lagoon emission flux rates, building fluxes, on a floor area basis, were considered equal to lagoon flux rates. The emission factor was applied to all CAFOs surrounding the original 4 sites and surrounding an additional 6 sites in Iowa, producing an average modeled-to-measured RHC ratio of 1.24. When the emission factor was applied to AERMOD to simulate the spatial distribution of hydrogen sulfide around a hypothetical large swine CAFO (1M kg), concentrations 0.5 km from the CAFO were 35 ppb and dropped to 2 ppb within 6 km of the CAFO. These values compare to a level of 30 ppb that has been determined by the State of Iowa as a threshold level for ambient hydrogen sulfide levels.     

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%).     

Modelling the influence of peri-urban trees in the air quality of Madrid region (Spain)

Tropospheric ozone (O(3)) is considered one of the most important air pollutants affecting human health. The role of peri-urban vegetation in modifying O(3) concentrations has been analyzed in the Madrid region (Spain) using the V200603par-rc1 version of the CHIMERE air quality model. The 3.7 version of the MM5 meteorological model was used to provide meteorological input data to the CHIMERE. The emissions were derived from the EMEP database for 2003. Land use data and the stomatal conductance model included in CHIMERE were modified according to the latest information available for the study area. Two cases were considered for the period April-September 2003: (1) actual land use and (2) a fictitious scenario where El Pardo peri-urban forest was converted to bare-soil. The results show that El Pardo forest constitutes a sink of O(3) since removing this green area increased O(3) levels over the modified area and over down-wind surrounding areas.     

Organic pollutants in the Odra river ecosystem

The paper presents the results of the analysis of water and bottom sediment samples from different locations along the Odra river, collected during eight campaigns in the years 1997-2000. All the basic organic pollutants were determined. Pollution of water with PCBs was negligible while with PAHs was not critical. Concentrations of volatile organochlorine compounds in water were the highest in Brzeg Dolny and its surroundings. Concentrations of tetrachloroethene were exceptionally high in the Szczecin Lagoon. The Odra sediments were non-polluted or only slightly polluted with PCBs, pesticides and different classes of volatile organic pollutants. However, PAHs were present in rather large concentrations, especially in the upper course of the Odra river. The data obtained contributed to the evaluation of pollutants discharges to the Baltic sea via Szczecin Lagoon as well as to the characterization of pollution sources (point sources and area sources) in the Odra catchment area.     

环境监测优化布点的投影寻踪回归分析法

应用投影寻踪回归（ＰＰＲ） 建模技术,分析环境测点的污染物监测数据之间的关系。通过建模过程中的数据生成,获得各测点的数据对环境质量总体状况的权重贡献率。根据权重贡献大小,进行环境测点的优选。用ＰＰＲ 分析法从成都市３ 项大气污染物的１２ 个环境测点中,优选出５ 个测点,其保留信息量约占全部测点信息量的９０ ％ 。     

Apportionment of Ambient Primary and Secondary Fine Particulate Matter during a 2001 Summer Intensive Study at the CMU Supersite and NETL Pittsburgh Site

Abstract

Gaseous and particulate pollutant concentrations associated with five samples per day collected during a July 2001 summer intensive study at the Pittsburgh Carnegie Mellon University (CMU) Supersite were used to apportion fine particulate matter (PM_2.5) into primary and secondary contributions using PMF2. Input to the PMF2 analysis included the concentrations of PM_2.5 nonvolatile and semivolatile organic material, elemental carbon (EC), ammonium sulfate, trace element components, gas-phase organic material, and NO_x, NO₂, and O₃ concentrations. A total of 10 factors were identified. These factors are associated with emissions from various sources and facilities including crustal material, gasoline combustion, diesel combustion, and three nearby sources high in trace metals. In addition, four secondary sources were identified, three of which were associated with secondary products of local emissions and were dominated by organic material and one of which was dominated by secondary ammonium sulfate transported to the CMU site from the west and southwest. The three largest contributors to PM_2.5 were sec ondary transported material (dominated by ammonium sulfate) from the west and southwest (49%), secondary material formed during midday photochemical processes (24%), and gasoline combustion emissions (11%). The other seven sources accounted for the remaining 16% of the PM_2.5. Results obtained at the CMU site were comparable to results previously reported at the National Energy Technology Laboratory (NETL), located approximately 18 km south of downtown Pittsburgh. The major contributor at both sites was material transported from the west and southwest. Some difference in nearby sources could be attributed to meteorology as evaluated by HYSPLIT model back-trajectory calculations. These findings are consistent with the majority of the secondary ammonium sulfate in the Pittsburgh area being the result of contributions from distant transport, and thus decoupled from local activity involving organic pollutants in the metropolitan area. In contrast, the major local secondary sources were dominated by organic material.     

Application of the tracer-aerosol gradient interpretive technique to sulfur attribution for the big bend regional aerosol and visibility observational study

A simple data analysis method called the Tracer-Aerosol Gradient Interpretive Technique (TAGIT) is used to attribute particulate S and SO2 at Big Bend National Park in Texas and nearby areas to local and regional sources. Particulate S at Big Bend is of concern because of its effects on atmospheric visibility. The analysis used particulate S, SO2, and perfluorocarbon tracer data from six 6-hr sampling sites in and near Big Bend National Park. The data were collected in support of the Big Bend Regional Aerosol and Visibility Observational (BRAVO) Study; the field portion was conducted from July through October 1999. Perfluorocarbon tracer was released continuously from a tower at Eagle Pass, TX, approximately 25 km northeast of two large coal-fired power plants (Carbon I and II) in Coahuila, Mexico, and approximately 270 km east-southeast of Big Bend National Park. The perfluorocarbon tracer did not properly represent the location of the emissions from the Carbon power plants for individual 6-hr sampling periods and attributed only 3% of the particulate S and 27% of the SO2 at the 6-hr sites in and near Big Bend to sources represented by the tracer. An alternative approach using SO2 to tag "local" sources such as the Carbon plants attributed 10% of the particulate S and 75% of the SO2 at the 6-hr sites to local sources. Based on these two approaches, most of the regional (65-86%) and a small fraction (19-31%) of the local SO2 was converted to particulate S. The analysis implies that substantial reductions in particulate S at Big Bend National Park cannot be achieved by only reducing emissions from the Carbon power plants; reduction of emissions from many sources over a regional area would be necessary.     

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%).