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.

     

A fluctuating plume dispersion model for the prediction of odour-impact frequencies from continuous stationary sources

A fluctuating plume dispersion model has been developed to facilitate the prediction of odour-impact frequencies in the communities surrounding elevated point sources. The model was used to predict the frequencies of occurrence of odours of various magnitudes for 1 h periods. In addition, the model predicted the maximum odour level. The model was tested with an extensive set of data collected in the residential areas surrounding the paint shop of an automotive assembly plant. Most of the perceived odours in the vicinity of the 64, 46 m high stacks ranged between 2 and 7 odour units and generally persisted for less than 30 s. Ninety-eight different field determinations of odour impact frequencies within 1 km of the plant were conducted during the course of the study. To simplify evaluation, the frequencies of occurrence of different odour levels were summed to give the total frequency of occurrence of all readily detectable (>2 OU) odours. The model provided excellent simulation of the total frequencies of occurrence where the odour was frequent (i.e. readily detectable more than 30% of the time). At lower frequencies of occurrence the model prediction was poor. The stability class did not seem to affect the model's ability to predict field frequency values. However, the model provided excellent predictions of the maximum odour levels without being sensitive to either stability class or distance from the source. Ninety-five percent of the predicted maximum values were within a factor of two of the measured field maximum values.     

Appropriateness of selecting different averaging times for modelling chronic and acute exposure to environmental odours

Odour emissions are episodic, characterised by periods of high emission rates, interspersed with periods of low emissions. It is frequently the short term, high concentration peaks that result in annoyance in the surrounding population. Dispersion modelling is accepted as a useful tool for odour impact assessment, and two approaches can be adopted. The first approach of modelling the hourly average concentration can underestimate total odour concentration peaks, resulting in annoyance and complaints. The second modelling approach involves the use of short averaging times. This study assesses the appropriateness of using different averaging times to model the dispersion of odour from a landfill site. We also examine perception of odour in the community in conjunction with the modelled odour dispersal, by using community monitors to record incidents of odour. The results show that with the shorter averaging times, the modelled pattern of dispersal reflects the pattern of observed odour incidents recorded in the community monitoring database, with the modelled odour dispersing further in a north easterly direction. Therefore, the current regulatory method of dispersion modelling, using hourly averaging times, is less successful at capturing peak concentrations, and does not capture the pattern of odour emission as indicated by the community monitoring database. The use of short averaging times is therefore of greater value in predicting the likely nuisance impact of an odour source and in framing appropriate regulatory controls.     

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%).     

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.     

Which meteorological conditions produce worst-case odors from area sources?

Two competing meteorological factors influence atmospheric concentrations of pollutants from open liquid area sources such as wastewater treatment plant units: temperature and stability. High temperatures in summer produce greater emissions from liquid area sources because of increased compound volatility; however, these emissions tend to disperse more readily because of frequent occurrence of unstable conditions. An opposite scenario occurs in winter, with lesser emissions due to lower temperatures, but also frequently less dispersion, due to stable atmospheric conditions. The primary objective of this modeling study was thus to determine whether higher atmospheric concentrations from open liquid area sources occur more frequently in summer, when emissions are greater but so is dispersion, or in winter, when emissions are lesser but so is dispersion. The study utilized a rectangular clarifier emitting hydrogen sulfide as a sample open liquid area source. Dispersion modeling runs were conducted using ISCST3 and AERMOD, encompassing 5 yr of hourly meteorological data divided by season. Emission rates were varied hourly on the basis of a curve-fit developed from previously collected field data. Model output for each season was used to determine (1) maximum 2-min average concentrations, (2) the number of odor events (2-min average concentrations greater than odor detection thresholds), and (3) areas of impact. On the basis of these 3 types of output, it was found that the worst-case odors were associated with summer, considering impacts of meteorology upon both emissions and dispersion. Not accounting for the impact of meteorology on emissions (using a constant worst-case emission rate) caused concentrations to be overpredicted compared with a variable emission rate case. The highest concentrations occurred during stability classes D, E, and F, as anticipated. A comparison of ISCST3 and AERMOD found that for the area source modeled, ISCST3 predicted higher concentrations and more odor events for all seasons.     

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.     

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%).     

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.     

Modeling Emissions from Elevated Cylindrical Bins

Abstract

This paper demonstrates how wind tunnel modeling data that accurately describe plume characteristics near an unconventional emission source can be used to improve the near-field downwind plume profiles predicted by conventional air pollution dispersion models. The study considers a vertical, cylindrical-shaped, elevated bin similar to large product storage bins that can be found at many industrial plant sites. Two dispersion models are considered: the U.S. Environmental Protection Agency's ISC2(ST) model and the Ontario Ministry of the Environment and Energy's GAS model. The wind tunnel study showed that plume behavior was contrary to what was predicted using conventional dispersion models such as ISC2(ST) and GAS and default values of input parameters. The wind tunnel data were used to develop a protocol for correcting the dispersion models inputs, resulting in a substantial improvement in the accuracy of the dispersion estimates.     

Source location and characterization of volatile organic compound emissions at a petrochemical plant in Kaohsiung, Taiwan

This paper elucidated a novel approach to locating volatile organic compound (VOC) emission sources and characterizing their VOCs by database and contour plotting. The target of this survey was a petrochemical plant in Linyan, Kaohsiung County, Taiwan. Samples were taken with canisters from 25 sites inside this plant, twice per season, and analyzed by gas chromatography-mass spectrometry. The survey covered 1 whole year. By consolidated into a database, the data could be readily retrieved, statistically analyzed, and clearly presented in both table and graph forms. It followed from the cross-analysis of the database that the abundant types of VOCs were alkanes, alkenes/dienes, and aromatics, all of which accounted for 99% of total VOCs. By contour plotting, the emission sources for alkanes, aromatics, and alkenes/ dienes were successfully located. Through statistical analysis, the database could provide the range and 90% confidence interval of each species from each emission source. Both alkanes and alkene/dienes came from tank farm and naphtha cracking units and were mainly composed of C3-C5 members. Regarding aromatics, benzene, toluene, and xylenes were the primary species; they were emitted from tank farm, aromatic units, and xylene units.     

A method to estimate emission rates from industrial stacks based on neural networks

This paper presents a technique based on artificial neural networks (ANN) to estimate pollutant rates of emission from industrial stacks, on the basis of pollutant concentrations measured on the ground. The ANN is trained on data generated by the ISCST3 model, widely accepted for evaluation of dispersion of primary pollutants as a part of an environmental impact study. Simulations using theoretical values and comparison with field data are done, obtaining good results in both cases at predicting emission rates. The application of this technique would allow the local environment authority to control emissions from industrial plants without need of performing direct measurements inside the plant.     

(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.     

Study of the relationship of distant SO2 emissions to Dallas-Fort Worth winter haze

A study was conducted to estimate the changes in wintertime visual air quality in Dallas-Fort Worth (DFW) that might occur due to proposed reductions in SO2 emissions at two steam electric generating plants in eastern Texas, each over 100 km from the city. To provide information for designing subsequent investigations, the haze was characterized broadly during the first year of the study. Meteorological data acquired then demonstrated that, during haze episodes, emissions from only one of the two plants were likely to be transported directly to DFW. Therefore, the second year of the study was centered on just one of the power plants. Air quality was then characterized within the urban area and at rural locations that would be upwind and downwind of the plant during transport to DFW. An instrumented aircraft measured plume dispersion and the air surrounding the plume on selected days. A mathematical model was used to predict the change that would occur in airborne particulate matter concentrations in DFW if SO2 emissions were reduced to reflect the proposed limitations. The contribution of particles in the atmosphere to light extinction was estimated, and simulated photographs were produced to illustrate the visibility changes. The study concluded that the proposed emission reductions would, at most, subtly change perceived wintertime visibility.     

Source Location and Characterization of Volatile Organic Compound Emissions at a Petrochemical Plant in Kaohsiung,Taiwan

Abstract

This paper elucidated a novel approach to locating volatile organic compound (VOC) emission sources and characterizing their VOCs by database and contour plotting. The target of this survey was a petrochemical plant in Linyan, Kaohsiung County, Taiwan. Samples were taken with canisters from 25 sites inside this plant, twice per season, and analyzed by gas chromatography–mass spectrometry. The survey covered 1 whole year. By consolidated into a database, the data could be readily retrieved, statistically analyzed, and clearly presented in both table and graph forms. It followed from the cross‐analysis of the database that the abundant types of VOCs were alkanes, alkenes/dienes, and aromatics, all of which accounted for 99% of total VOCs. By contour plotting, the emission sources for alkanes, aromatics, and alkenes/dienes were successfully located. Through statistical analysis, the database could provide the range and 90% confidence interval of each species from each emission source. Both alkanes and alkene/dienes came from tank farm and naphtha cracking units and were mainly composed of C₃–C₅ members. Regarding aromatics, benzene, toluene, and xylenes were the primary species; they were emitted from tank farm, aromatic units, and xylene units.     

Study of the Relationship of Distant SO2 Emissions to Dallas-Fort Worth Winter Haze

ABSTRACT

A study was conducted to estimate the changes in wintertime visual air quality in Dallas-Fort Worth (DFW) that might occur due to proposed reductions in SO₂ emissions at two steam electric generating plants in eastern Texas, each over 100 km from the city. To provide information for designing subsequent investigations, the haze was characterized broadly during the first year of the study. Meteorological data acquired then demonstrated that, during haze episodes, emissions from only one of the two plants were likely to be transported directly to DFW. Therefore, the second year of the study was centered on just one of the power plants. Air quality was then characterized within the urban area and at rural locations that would be upwind and downwind of the plant during transport to DFW. An instrumented aircraft measured plume dispersion and the air surrounding the plume on selected days. A mathematical model was used to predict the change that would occur in airborne particulate matter concentrations in DFW if SO₂ emissions were reduced to reflect the proposed limitations. The contribution of particles in the atmosphere to light extinction was estimated, and simulated photographs were produced to illustrate the visibility changes. The study concluded that the proposed emission reductions would, at most, subtly change perceived wintertime visibility.     

Model performance—Plume dispersion over elevated terrain

A study was conducted to evaluate the ability of a steady-state Gaussian model (MVM) to simulate plume dispersion over high terrain. Modeling results are compared with measured data from a network of monitors around the Tennessee Valley Authority (TVA) Widows Creek Steam Plant. Approximately 2.5 years of monitoring and plant emissions data were used in the study.The MVM modeling results are also compared with results using the RTDM and COMPLEX 1 models. The MVM model and an updated version compared well with measured data. RTDM and the COMPLEX 1 models overpredicted both the 1- and 3-h average concentration by a factor of two-three.     

Use of stationary and mobile measurements to study power plant emissions

This paper presents a technique to study air pollution by combining high spatial resolution data obtained by a mobile platform and those measured by conventional stationary stations. Conventional stations provide time-series point data but cannot yield information that is distant from the sites. This can be complemented or supplemented by mobile measurements in the vicinity of the conventional sites. Together, the combined dataset yields a clearer and more precise picture of the dispersion and the transformation of pollutants in the atmosphere in a fixed time frame. Several experiments were conducted in the years 2002-2003 to track the impact of power plant plumes on ground receptors in the immediate vicinity (within a radius of 30 km) of the plants, using a combined mobile and stationary dataset. The mobile data allowed the identification of emissions from coal-fired and gas-fired power plants. Coal-fired power plants were the major source of sulfur dioxide (SO2), whereas nitrogen oxides (NOx) emitted from the gas-fired power plant played an important role in the formation of ozone (O3) at ground level. The mobile data showed that two particle size distribution regimes were detected: one had a dominant accumulation mode at 0.40-0.65 microm and the other at 0.65-1 microm. The existence of particles characterized by their mode at 0.65-1 microm and formed by in-cloud processes suggests that vehicular emissions were not the important source. Other local sources, such as power plants (elevated emission), were the likely sources, because Hong Kong does not have much manufacturing industry.     

Dose-response relationship in lethal and behavioural effects of different insecticides on the parasitic wasp Aphidius ervi

Neurotoxic insecticides are widely used for crop protection and behavioural perturbations can be expected in surviving beneficial insects, including parasitoids of pest insects. The present study aims to investigate the relationship between the dose of insecticide parasitoids have been exposed to, and the subsequent ability of these parasitoids to respond to host-related cues. A four-armed olfactometer, a design widely used to observe orientation responses in various insects and parasitoids in particular, was chosen to investigate the dose-response relationship. The species studied was Aphidius ervi, a relatively generalist parasitoid of aphids, and commercialised for biological control and integrated pest management. Active ingredients with similar and different modes of action on the nervous system were compared: a pyrethroid (lambda-cyhalothrin), an organophosphate (chlorpyrifos), a carbamate (pirimicarb) and a carbamyltriazole (triazamate). Adult females were exposed to dry residues on glass for 24 h. LD50 were calculated and predicted a high risk of mortality at the field application rate. The effect of five increasing residual doses of each active ingredient was tested on responses to plant-host odour in the olfactometer, from sublethal doses to LD50, and up to LD70 for some products. It appeared that none of the doses of lambda-cyhalothrin, chlorpyriphos and pirimicarb had any effect on A. ervi responses to the odour from the aphid-infested plant (Myzus persicae on oilseed rape). But for triazamate, a significant dose-behavioural response was quantified and attraction to the odour was no longer significant in females surviving the LD50. The possible explanations for the presence or absence of effect, depending on the insecticide are discussed.     

Understanding oil and gas pneumatic controllers in the Denver–Julesburg basin using optical gas imaging

ABSTRACT

In the spring of 2018, a 10-day field study was conducted in Colorado’s Denver-Julesburg oil and natural gas production basin to improve information on well pad pneumatic controller (PC) populations and identify PCs with potential maintenance issues (MIs) causing excess emissions through a novel optical gas imaging (OGI) survey approach. A total of 500 natural gas-emitting PCs servicing 102 wells (4.9 PCs/well) were surveyed at 31 facilities operated by seven different companies. The PCs were characterized by their designed operational function and applications, with 83% of the PC population identified as intermittent PCs (IPCs). An OGI inspection protocol was used to investigate emissions on 447 working PCs from this set. OGI detected continuous emissions from 11.3% of observed IPCs and these were classified as experiencing some level of MIs. OGI imaging modes were observed to have a significant effect on emission detectability with high sensitivity mode detection rates being approximately 2 times higher compared to auto mode. Fourteen snapshot emission measurements (not including actuations) were conducted on IPCs in this category using a high-volume sampling device with augmented quality assurance procedures with observed emissions rates ranging from 0.1 up to 31.3 scf/hr (mean = 2.8 scf/hr). For PCs with continuous depressurization type (CPC), 36.8% had continuous emissions observed by OGI. Four supporting emission measurements were conducted on CPCs with one unit exceeding the low bleed regulatory emission threshold with an emission rate of 9.9 scf/hr (mean = 4.2 scf/hr). Additional information was collected on PC actuation events, as observed with OGI, which showed a strong correlation between observed actuation events and facility production compared to observed continuous emissions caused by MIs which did not correlate with facility production.

Implications: A novel survey approach of pneumatic controllers at oil and natural gas production facilities in the Denver-Julesburg basin, using optical gas imaging and supporting emission measurements, was demonstrated as an effective method to identify controllers with potential maintenance issues causing excess emissions. The results of the pneumatic controller and optical gas imaging surveys improved information on pneumatic controller populations within the basin and also demonstrated the significant effect optical gas imaging modes have on emission detections.