Air Dispersion Model Research Articles

A review on the use of air dispersion models for odour assessment

Only recently have unpleasant odours become an important environmental issue and a specific legislation is being developed and implemented. The assessment of odour impact involves several different aspects (large number of odorants, human sensitivity, different perception thresholds, etc.) and it is dealt with by the application of different techniques and methods. In this paper a review about the reliability of air dispersion models applied to odour impact assessment determined by different type of sources is presented, based on the analysis of 69 case studies published over the last 10 years. The Gaussian models were the most used technique (67% of case studies) often accompanied with results validation with experimental data. The analysis of the case studies and related validation processes highlighted the reliability of dispersion models if some critical issues, such as the presence of particular climate conditions, duration of averaging times and position of important receptors, are known and correctly managed.

Air dispersion modelling of gas turbine power plant emissions in Makassar by using AERMOD

Activities at PT. PLN (Persero) Makassar Tello Sector especially Gas Turbine Power Plant produces exhaust emissions that can be pollution for the environment if the quality is not monitored. Therefore, an analysis of the pattern distribution of exhaust emissions in Gas Turbine Power Plant needs to be done to determine the concentration produced and dispersion pattern and direction of its spread. The modeling in this study uses AERMOD software and requires meteorological support data and ground elevation to run the software. The results showed that the distribution of SO2, NO2, CO, and particulates gas pollutants was in line with the dominant wind direction found in windrose. The concentration received by the receptors is very small and does not exceed the ambient air quality standards based on South Sulawesi Governor Regulation Number 69 of 2010 which is 365 μg/Nm3 for SO2, 150 μg/Nm3 for NO2, 10,000 μg/Nm3 for CO, and 230 μg/Nm3 for particulates. Thus, the impact received by the receptor is not felt directly.

Estimating exposure to fine particulate matter emissions from vehicle traffic: Exposure misclassification and daily activity patterns in a large, sprawling region

Vehicle traffic is responsible for a significant portion of toxic air pollution in urban areas that has been linked to a wide range of adverse health outcomes. Most vehicle air quality analyses used for transportation planning and health effect studies estimate exposure from the measured or modeled concentration of an air pollutant at a person's home. This study evaluates exposure to fine particulate matter from vehicle traffic and the magnitude and cause of exposure misclassification that result from not accounting for population mobility during the day in a large, sprawling region. We develop a dynamic exposure model by integrating activity-based travel demand, vehicle emission, and air dispersion models to evaluate the magnitude, components and spatial patterns of vehicle exposure misclassification in the Atlanta, Georgia metropolitan area. Overall, we find that population exposure estimates increase by 51% when population mobility is accounted for. Errors are much larger in suburban and rural areas where exposure is underestimated while exposure may be overestimated near high volume roadways and in the urban core. Exposure while at work and traveling account for much of the error. We find much larger errors than prior studies, all of which have focused on more compact urban regions. Since many people spend a large part of their day away from their homes and vehicle emissions are known to create "hotspots" along roadways, home-based exposure is unlikely to be a robust estimator of a person's actual exposure. Accounting for population mobility in vehicle emission exposure studies may reveal more effective mitigation strategies, important differences in exposure between population groups with different travel patterns, and reduce exposure misclassification in health studies.

Flammability risk assessment for oil spill response operations

Immediately following a spill at sea, released oil—ranging from diesel to light crude and diluted bitumen, will initially weather through evaporation, resulting in an elevated concentration of light hydrocarbons in the air. As part of oil spill response operations, first responders use hand-held devices to monitor airborne concentrations when approaching a spill. The feasibility of using numerical modelling as an additional tool to assess potential flammability and plan response operations in the spill area was explored in this study. The Lower Explosive Limit (LEL) is defined as the minimum concentration of a gas in air, in this case a mixture of evaporated hydrocarbons, which can produce a flash fire in the presence of an ignition source. This ignition source could be triggered by the vessel itself or by spill response operations. A framework was put into place, utilizing a threedimensional hydrodynamic model (H3D), an oil spill model (SPILLCALC), and an air dispersion model (CALPUFF) to assess the risk of possible ignition of the hydrocarbon vapour in the event of a spill. The study looked at a hypothetical credible worst case tanker spill (16 500 m3) of diluted bitumen (cold lake winter blend) occurring at Arachne Reef in Haro Strait, British Columbia, Canada. SPILLCALC provided one-minute averaged vapour fluxes from the water surface for each of 17 modelled pseudo-components which were used as inputs to CALPUFF. Using the predicted airborne concentrations of each pseudo-component, time-scaled to one-second averages, the flammability potential in the immediate spill area was determined at each grid point using Le Chatelier’s mixing equation. The approach describe here was developed as a proof of concept, and could be established as a real-time system, bringing valuable information in addition to hand-held devices during a spill response, or during a response exercise. This modelling study was conducted as part of Kinder Morgan’s Trans Mountain Pipeline Expansion Project. There are a number of commercially available oil spill models but few if any are equipped with the ability to model air dispersion and forecast hazardous conditions as discussed in this paper.

Development of Reduction Scenarios Based on Urban Emission Estimation and Dispersion of Exhaust Pollutants from Light Duty Public Transport: Case of Tabriz, Iran

The transportation sector is one of the major air pollution sources in cities and accounts for about 72% of total air pollution in Tabriz, Iran. The economic burden of air pollution on Iran’s economy was estimated to be 8 and 10 billion dollars in 2005 and 2010, respectively. Using air pollution models, including emission and dispersion models, has been proposed as a logical solution to overcome problems such as expensive measurement methods and in some cases, difficulty and impossibility of direct measurements. In this paper, IVE emission model was used for estimating emission amounts and emission factors of taxi fleet of Imam Khomeini Street, one of main and busiest streets in Tabriz, as a representative street and for studying effects of different scenarios. AERMOD dispersion model was used for investigating the dispersion pattern of emitted pollutants from this fleet and to estimate the effect of using local base emission factors and replacement of present taxi fleet by environmental friendly vehicles in this city. CO, CH4, and NOx pollutants with quantities of 108, 20.7, and 5.4 g/km have the highest emission factor among all pollutants. Amount of hourly emissions per each pollutant, daily emission amount by technology types, and daily emission amount of each technology by pollutant types were also examined. CO emission has the highest amount at 7–8 in the morning, and at noon hours, emission of NOx and VOC increases. Technologies with old fuel injection and emission control systems and higher mileage have higher level of pollution emission. Investigating dispersion of pollutants from this fleet in the atmosphere shows pollutants movement in the northwest direction. Performance statistics of AERMOD model, such as FB, NMSE, MG, VG, FAC2, and R by use of pollution level of two adjacent air pollution monitoring stations were determined to continue. Three air pollution scenarios are used to assay effects of using site-specific base emission factors; replacement of worn-out portion of fleet with two distinct technology types (gasoline fuel and CNG fuel) were investigated. As a result under the first and second scenarios, 6.4% and 3.9% decrease of GWP would be estimated respectively and under the third scenario, 7.8% increase of GWP relative to the present study was observed.

Human continuous hydrogen cyanide inhalation predictor with a physiologically based pharmacokinetic (PBPK) model.

Hydrogen cyanide (HCN) is volatile and highly toxic with acute and chronic effects on humans. Gaseous HCN enters the atmosphere from natural processes or industrial activities, which lead to human exposure. Effective intervention in cases of HCN inhalation requires an efficient diagnostic tool. The existing physiologically based pharmacokinetic (PBPK) model for HCN cannot clearly simulate continuous HCN inhalation or predict HCN levels in inhaled air. The current study presents a PBPK model for continuous inhalation of HCN, called Human Continuous Cyanide Inhalation Predictor (HCCIP). Since existing data on pharmacokinetics of HCN inhalation are limited, HCCIP utilizes extensive data from the current authors' PBPK model on cyanide ingestion. The structure of HCCIP comprises the lungs, kidneys, liver, and slowly perfused tissue. In both the human body and in exhaled air, HCCIP features the ability to predict concentration-time courses of cyanide. Moreover, HCCIP can predict HCN concentration in inhaled air from known blood cyanide levels. After completion, the results of HCCIP were validated against preexisting published datasets. The simulation results agreed with these datasets, validating the model. The HCCIP model is an effective tool for assessing risk from continuous HCN inhalation, and HCCIP extends the capabilities of air dispersion modeling, such as AERMOD or CALPUFF, to assess HCN risk from specific release sources.

Assessment for health equity of PM2.5 exposure in bikeshare systems: The case of Divvy in Chicago

IntroductionBikeshare is becoming more and more popular around the world. Many cities in the United States have implemented their own bikeshare systems or are considering having one. Among many other benefits by bikeshare, physical health improvement has been mentioned as one of them as a common sense. However, not enough attention has been paid to potential health impacts when using bikeshare. This is not even to mention how the health impact is distributed among different groups. To address this research gap, we conducted a preliminary study to analyze the uneven distribution of health impacts by making bikeshare trips. MethodIn our research, we chose the Divvy bikeshare system in Chicago since it is currently one of the biggest systems in the USA and its trip data are open. By incorporating emission and air dispersion modeling tools, we first estimated the high-resolution air pollution concentration level in the city. Then, we quantified the trip-based PM2.5 exposure by including every trip route and duration time. Finally, we conducted a spatial analysis for health exposure related to bikeshare trips in disadvantaged areas. ResultsIn Chicago, most of routes with high PM2.5 exposure index are distributed in the southwest of Chicago, where there are more minority populations or low-income communities. From the station level, most of the stations in disadvantaged areas have a high level of PM2.5 exposure index on average. ConclusionsOur research has clearly shown that users from disadvantaged areas are more likely to take a risk of absorbing more PM2.5, especially when traveling to other areas with more job opportunities and other essential services by bikeshare. In summary, our research points out an ignored aspect in planning bikeshare. Bicycle infrastructure design and air pollution control should be integrated into the process of bikeshare promotion in disadvantaged areas.

Dispersion modeling of odour, gases, and respirable dust using AERMOD for poultry and dairy barns in the Canadian Prairies

For determining setback distances considering multiple air pollutants, a comprehensive study was conducted to simulate the atmospheric dispersion of odour, ammonia (NH3), hydrogen sulfide (H2S), and respirable dust using an US EPA air dispersion model AERMOD for a commercial dairy, broiler, and cage-layer barn in the Canadian Prairies. The simulation was conducted using five years of meteorological data. Setback distances were determined with the input of varying monthly emission rates of all four air pollutants and odour impact criteria specifically developed for all three odour sources. Results showed the layer barn had the greatest odour impact area (maximum 3023 m for an annual average odour concentration of 0.01 OU m−3) followed by the broiler and dairy barns. Due to the prevailing south wind for all three barns, odour traveled farthest in the north. Using the suggested odour impact criteria by the Government of Saskatchewan defined for all odour sources, maximum setback distances were decreasing from 1941 to 641 m for the layer barn and from 980 to 320 m for the broiler barn along with the increasing of odour concentration (OC) thresholds (1–6 OU m−3), all in the north direction. While for the dairy barn, setback distances were determined only under an OC limit of 1 OU m−3; maximum 205 m in the north and minimum 171 m in the south. Using the newly developed odour impact criteria specifically for the three odour sources, maximum setback distance of 558 m in the north was determined for the layer barn under an odour threshold of 9 OU m−3. Additionally, the results suggest the use of odour impact criteria for determining setback distance rather than using gas/respirable threshold limits set in ambient air quality standards as the former always requires much greater setback distances than the latter.

A Procedure to Select Meteorological Data for Air Dispersion Modeling of Pesticide Applications in California.

This study developed a procedure to select a set of 5-y meteorological data with the potential to estimate the highest concentrations ("worst-case scenario") in air dispersion modeling of pesticide applications with American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD). The study analyzed the relationship between the 95th percentile maximum concentrations estimated by AERMOD and the percentages of low wind speed (LWS, 0.5-2 m/s) in the meteorological data used for the modeling. Statistical analysis showed that they were positively correlated within various distances to different types of emission sources. In addition, the LWS percentages of 1-y data could be used to predict the LWS percentages of 5-y data for the same station. Based on these results, the selection procedure for meteorological data began with the evaluation of 1-y data quality and LWS percentages for all the available stations in California, USA, counties with high use of a pesticide of interest. Five-year meteorological data were then processed for the top 5 stations with the highest LWS percentages to perform AERMOD modeling. Finally, the air concentration estimates of the modeled meteorological data were compared to determine the worst-case scenario data. This procedure provided a strategic plan for selecting meteorological data for AERMOD modeling of pesticide applications in California. The procedure was applied to the modeling of residential structural fumigations and determined that the 5-y (2011-2015) data of the weather station WBAN 93134 (downtown Los Angeles, University of Southern California campus) was the worst-case scenario meteorological data for this modeling case. Integr Environ Assess Manag 2019;15:648-658. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.

Determination of the Area Affected by Agricultural Burning

Agricultural burning is still a common practice around the world. It is associated with the high emission of air pollutants, including short-term climate change forcing pollutants such as black carbon and PM2.5. The legal requirements to start any regulatory actions to control them is the identification of its area of influence. However, this task is challenging from the experimental and modeling point of view, since it is a short-term event with a moving area source of pollutants. In this work, we assessed this agricultural burning influence-area using the US Environmental authorities recommended air dispersion model (AERMOD). We considered different sizes and geometries of burning areas located on flat terrains, and several crops burning under the worst-case scenario of meteorological conditions. The influence area was determined as the largest area where the short-term concentrations of pollutants (1 h or one day) exceed the local air quality standards. We found that this area is a band around the burning area whose size increases with the burning rate but not with its size. Finally, we suggested alternatives of public policy to regulate this activity, which is based on limiting the burning-rate in the way that no existing households remain inside the resulting influence-area. However, this policy should be understood as a transition towards a policy that forbids agricultural burning.

Modelling Environmental Impacts of Cesium-137 Under a Hypothetical Release of Radioactive Waste.

Waste tanks at the nuclear facility located at Sellafield, UK, represent a nuclear source which could release radionuclides to the atmosphere. A model chain which combines atmospheric transport, deposition as well as riverine transport to sea has been developed to predict the riverine activityconcentrations of 137Cs. The source term was estimated to be 9 × 104TBq of 137Cs, or 1% of the assumed total 137Cs inventory of the HAL (Highly Active Liquid) storage tanks. Air dispersion modelling predicted 137Cs deposition reaching 127kBqm-2 at the Vikedal catchment in Western Norway. Thus, the riverine transport model predicted that the activity concentration of 137Cs inwater at the river outlet could reach 9000Bqm-3 in the aqueous phase and 1000Bqkg-1 in solid phase at peak level. The lake and river reaches showed different transport patterns due to the buffering effects caused by dilution and slowing down of water velocity.

Pseudo-source parameters for flares: Derivation, implementation, and comparison

ABSTRACTThe United States Environmental Protection Agency (US EPA) flare pseudo-source parameters are over 30 years old and few dispersion modellers understand their basis and underlying assumptions. The calculation of plume rise from the user inputs of pseudo-stack diameter, temperature and velocity have the most influence on air dispersion model predictions of ground-level concentrations. Regulatory jurisdictions across Canada, the United States and around the world have adopted their own approach to pseudo-source parameters for flares; all relate buoyancy flux to the heat release rate, none consider momentum flux and flare tip downwash as adopted by the Alberta Energy Regulator (AER). This paper derives the plume buoyancy flux for flares burning a gas in terms of combustion variables readily known or calculated without simplifying assumptions. Dispersion model prediction sensitivity to flared gas composition, temperature and velocity, and ambient conditions are now correctly handled by the AER approach. The AER flare pseudo-source parameters are based on both the buoyancy and momentum flux, thus conserving energy and momentum. The AER approach to calculate the effective source height for flares during varying wind speeds is compared to the US EPA approach. Instead of a constant source for all meteorological conditions, multiple co-located sources with varying effective stack height and diameter are used. AERMOD is run with the no stack tip downwash option as flare stack tip downwash is accounted for in the effective stack height rather than the AERMOD model calculating the downwash incorrectly using the pseudo-source parameters. The modelling approaches are compared for an example flare. Maximum ground level predictions change, generally increasing near the source and decreasing further away, with the AER flare pseudo-source parameters. It's time to update how we model flares.Implications: What are the implications of continuing to model flare source parameters using the overly simplified US EPA approach? First, the regulators perpetuate the myths that the flare source height, temperature, diameter and velocity are constant for all wind speeds and ambient temperatures. Second, that it is acceptable to make simplifying assumptions that violate the conservation of momentum and energy principles for the sake of convenience. Finally, regulatory decisions based on simplified source modelling result in predictions that are not conservative (or realistic). The AER regulatory approach for flare source parameters overcomes all of these shortcomings. AERflare is a publicly available spreadsheet that provides the “correct” inputs to AERMOD.

Modeling 1,3-D Concentrations in Ambient Air in High Use Airsheds of the United States

Soil fumigants, such as 1,3-dichloropropene (1,3-D), are used on a variety of different crops in high use areas in the United States, including the Pacific Northwest, the mid-Atlantic coast, and the Southeast coastal plains. Contaminant concentrations in air are often required for environmental exposure and human risk assessment. The SOil Fumigant Exposure Assessment (SOFEA) model, originally developed to explore volatile pesticide exposure and bystander risk, has recently been upgraded using AERMOD, the US Environmental Protection Agency’s (EPA) recommended regulatory air dispersion model to predict short-, medium-, and long-term pesticide concentrations in air resulting from representative agronomic practices in large airsheds. Modeling air concentrations has several advantages over monitoring such as the ability to predict concentrations at multiple locations and airsheds at a much greater temporal frequency than could be practically accomplished through monitoring alone. The agricultural modeling tool presented herein was parameterized using 1,3-D application data (mass, date, and depth applied, location, etc) obtained from growers in each study area, and local weather data and hourly concentrations of 1,3-D in ambient air were simulated for large airsheds. The human equivalent concentrations (HECs) for acute, short-term, sub-chronic, and chronic exposure of 1,3-D were not exceeded in any of the study areas investigated. These simulated 1,3-D concentrations are used to assess human exposure and risk, which considers human-life-stage-specific exposure factors, including residential mobility, time-activity patterns, and age-specific inhalation rates and body weights.

A Life Cycle Based Air Quality Modeling and Decision Support System (LCAQMS) for Sustainable Mining Management

Mining activities contribute the high level of air pollution at ground level and have significant environmental impacts. There is an urge to develop an integrated modeling system which helps to analyze these pollutants and their control strategies. Therefore, a new integrated approach is conceptualized as life cycle based air quality modeling system (LCAQMS) for the mining. This paper focuses on incorporating air quality modeling to understand the severity of air pollution in mining and developing an integrated system for mining related decision support with a field application. The system integrates inverse matrix which is used to develop air emission inventory; characterization method to assess the environmental implications; artificial neural network model for carbon footprint analysis; air dispersion modeling to predict the pollutant concentration at receptor level; and multicriteria decision analysis tool to provide air pollution control solutions. The developed LCAQMS method has applied to a copper mining site in the US. Inventory results reveal that NOX and SO2 produced more as compared to the other pollutants for this site. The study also helps to quantify the carbon credits for the copper mine. Prediction of the four significant pollutants (PM10, PM2.5, SO2, NOx) at ground level have been further calculated and validated with observed values at seven different monitoring stations. The modeling results have supported selecting the best alternative management technology for the air pollution control. It indicates that the developed LCAQMS can serve as a useful tool to assess, predict and assist in the selection of environmental mitigation options for mining sites.

Life Cycle Based Air Pollution Analysis and Management for Open Pit Mining

Mining activities are responsible for deteriorating the air quality at lo­cal or receptor level. In this paper, a life cycle based air quality modeling system (LCAQMS) for the mining is proposed. The aim of the model is to integrate differ­ent environmental models for understanding the impact of mining activities and THE effect of various atmospheric factors toward the vulnerability of air quality. The system is based on a life cycle assessment model, artificial neural network model, air dispersion model, and multicriteria decision model which is used to develop air emission inventory, carbon footprint analysis, prediction of the pol­lutant concentration, and air pollution control solutions respectively. The model is applied to the open pit copper mine, USA. The results reveal that NO x (39.6%), SO 2 (25.8%), CO (17%), PM 10 (10.7%), and PM 2.5 (3.6%) are the profound pollu­tants produced during copper mining. The study also helps to analyze the envi­ronmental impacts based on various indicators such as acidification and climate change. Prediction of pollutants at ground level have been determined and fur­ther validated with field data at different monitoring stations. Thus, the LCAQMS can serve as a mine-targeted air pollution model which helps to assess and pre­dict the air quality with the selection of a cost-effective solution for air pollution.

Site-specific (Multi-scenario) validation of ensemble Kalman filter-based source inversion through multi-direction wind tunnel experiments

Source inversion uses air dispersion models and environmental measurements to determine the atmospheric radionuclide release rate, which is critical in formulating an emergency response to nuclear incidents. Because source inversion methods are vulnerable to multiple uncertainties, site-specific validations that consider multiple air dispersion scenarios are important in ensuring their correct implementation. To comprehensively evaluate the ensemble Kalman filter (EnKF) for source inversion, a site-specific validation based on six wind tunnel experiments was performed for a highly heterogeneous nuclear power plant site in China. The six experiments cover the typical meteorology of the site and various topography types, providing abundant air dispersion scenarios for validation. The sensitivity of the EnKF to the initial guess, inflation factor, and position/number of measurements is also investigated. The results demonstrate that EnKF offers stable convergence and a reasonably bounded error in all experiments. Furthermore, the EnKF is insensitive to the initial guess, inflation factor, and number of measurements. However, it is sensitive to the position of the measurements and the air dispersion scenario. This sensitivity results from the complicated biases in air dispersion models, which highlight the key to improving the performance of EnKF.

A systematic knowledge synthesis on the spatial dimensions of Q fever epidemics.

From 2007 through 2010, the Netherlands experienced the largest Q fever epidemic ever reported. This study integrates the outcomes of a multidisciplinary research programme on spatial airborne transmission of Coxiella burnetii and reflects these outcomes in relation to other scientific Q fever studies worldwide. We have identified lessons learned and remaining knowledge gaps. This synthesis was structured according to the four steps of quantitative microbial risk assessment (QMRA): (a) Rapid source identification was improved by newly developed techniques using mathematical disease modelling; (b) source characterization efforts improved knowledge but did not provide accurate C. burnetii emission patterns; (c) ambient air sampling, dispersion and spatial modelling promoted exposure assessment; and (d) risk characterization was enabled by applying refined dose–response analyses. The results may support proper and timely risk assessment and risk management during future outbreaks, provided that accurate and structured data are available and exchanged readily between responsible actors.

Implementation of a New Protocol of Odour Field Investigations for the Paris Wastewater Treatment Plants

In the 90’s, the wastewater management authority for the Paris area (‘Syndicat interdepartemental pour l'assainissement de l'agglomeration parisienne’ - SIAAP) established various odour survey tools in order to understand the link between odours and significant operating events at its Waste Water Treatment Plant (WWTP) and in the sewage network. These tools include monitoring stations (weather stations, sulphur compounds analysed with stationary and mobile analytical instruments), human observers (daily odour patrols, voluntary citizens’ panels and citizens’ complaints logbook), and air dispersion modelling (three-dimensional numerical CFD (Computational Fluid Dynamics) dispersion allowing real-time and forecast odour dispersion).Starting in 2017, some important changes were initiated in order to improve this monitoring system. Indeed, as a response to important redesign works of one of their WWTPs (Seine Aval), the SIAAP needed to demonstrate a monitoring tool with a high level of confidence, especially during the crucial commissioning period.Because the previously operated daily patrols showed some limitations, one of the first steps was to implement a new protocol for field investigations and data treatment. The chosen method is, for the most part, inspired by the new standards: EN 16841-1 and EN 16841-2 - Determination of odour in ambient air by using field inspection - Grid method and Plume method. The originality of the new protocol implemented by SIAAP lies in the fact that both methods – Grid method and Plume method – are merged and adapted to needs and means of the SIAAP, in order to collect relevant data.

Integrating site-specific dispersion modeling into life cycle assessment, with a focus on inhalation risks in chemical production

ABSTRACTIt has become increasingly important for environmental managers to evaluate the human health (HH) impact of chemicals in their supply chain. Current life cycle assessment (LCA) methods are limited because they often only address the HH impact at large geographical scales. This paper aims to develop a method that derives a regionalized life cycle inventory data set and site-specific air dispersion modeling to evaluate the HH impact of chemicals along the life cycle phases at finer geographical scales to improve decision-making, with focus on inhalation pathway. More specifically, cancer risk and noncancer hazard index (HI) are quantified at the county level to identify high-risk regions and at the census tract level to reveal the geographical pattern of health impacts. The results showed that along the cradle-to-gate life cycle stages of a widely used chemical, methylene diphenyl diisocyanate (MDI), the accumulative inhalation risk was 3 orders of magnitude below the U.S. Environmental Protection Agency (EPA) risk management thresholds for both cancer risk (2.16 × 10−9) and noncancer HI (1.53 × 10−3). However, the absolute value of inhalation risks caused by the case study chemicals varied significantly in different geographical areas, up to 4 orders of magnitude. This paper demonstrates a feasible approach to improve human health impact assessment (HHIA) by combining site-specific air dispersion modeling and LCA using publicly available inventory data. This proposed method complements existing life cycle impact assessment (LCIA) models to improve HHIA by employing both HH risk assessment and LCA techniques. One potential outcome is to prioritize pollution prevention and risk reduction measures based on the risk maps derived from this method.Implications: It has become increasingly important for environmental managers to evaluate the human health impacts of chemicals in their supply chain. Regionalized life cycle inventory data sets should be developed using publically available databases such as EPA’s toxic release inventory. The combination of site-specific dispersion modeling and life cycle assessment modeling can improve human health impact assessment of chemicals by providing more regionalized results along their supply chain.

Vent pipe emissions from storage tanks at gas stations: Implications for setback distances

At gas stations, fuel vapors are released into the atmosphere from storage tanks through vent pipes. Little is known about when releases occur, their magnitude, and their potential health consequences. Our goals were to quantify vent pipe releases and examine exceedance of short-term exposure limits to benzene around gas stations. At two US gas stations, we measured volumetric vent pipe flow rates and pressure in the storage tank headspace at high temporal resolution for approximately three weeks. Based on the measured vent emission and meteorological data, we performed air dispersion modeling to obtain hourly atmospheric benzene levels. For the two gas stations, average vent emission factors were 0.17 and 0.21 kg of gasoline per 1000 L dispensed. Modeling suggests that at one gas station, a 1-hour Reference Exposure Level (REL) for benzene for the general population (8 ppb) was exceeded only closer than 50 m from the station's center. At the other gas station, the REL was exceeded on two different days and up to 160 m from the center, likely due to non-compliant bulk fuel deliveries. A minimum risk level for intermediate duration (>14–364 days) benzene exposure (6 ppb) was exceeded at the elevation of the vent pipe opening up to 7 and 8 m from the two gas stations. Recorded vent emission factors were >10 times higher than estimates used to derive setback distances for gas stations. Setback distances should be revisited to address temporal variability and pollution controls in vent emissions.