A statistical receptor‐oriented model was developed for long‐range transport of atmospheric sulfate to Dorset (elevation 320 m, latitude 4513′26″ N and longitude of 7855′52″ W), Ontario. This model computes the potential for sources within 1 latitude by 1 longitude grid cells across North America that contribute to the airborne concentrations measured at the ground station at Dorset. Airborne concentration data and air parcel backward trajectories were incorporated explicitly in the model calculation to identify the geographical areas of potential contributing sources. The present model is qualitative in nature; however, it provides a reasonable receptor‐oriented approach to examine the long‐range transport of atmospheric species. In order to fully understand the methodology and in a hope to optimize it, several aspects of the PSCF methodology have been examined in detailed in this study. Results of this study are presented that suggest interpolation of trajectory endpoints to increase the counting statistics for the potential source contribution function (PSCF) values is not reliable. The average concentration provides a reasonable criterion value; however, using the fiftieth percentile value as the criterion point provides an opportunity for identifying source areas that cannot be previously found by using the average concentration. The fiftieth percentile value may be a better choice for the particulate sulfate data in this case since Dorset is a relatively clean background site. Using the seventy‐fifth percentile, which is generally larger than the average, may not be suitable because it reduces the number of degrees of freedom. This could render the model to behave like a regular trajectory analysis model that has been used commonly for analyzing episodic pollution events. Separation of data into summer and winter periods is useful to illustrate the effects of photochemistry and meteorology on the PSCF results. Invoking the total probability concept and examining the trajectory arrival at different heights directly above the sampling site, the total PSCF was computed. This resultant function thus provides a time‐integrated geographical map useful for identifying sources of airborne particulate sulfate in a receptor‐oriented manner.
Optical gas imaging (OGI) is an effective tool for detecting gas leaks from process equipment. Despite the fact that OGI has been used for leak detection for over a decade, its detection limit is an elusive performance metric and has not been systematically characterized and quantified like other detection instruments. A substantial body of research has been performed that has shed some light on the OGI detection limits and the factors that dictate the detection limits. The OGI detection limit expressed as ppm-m and ppm now can be quantified as a function of ΔT (differential temperature between the gas plume and the background), the OGI camera type, and the specific gas in question. Furthermore, the OGI detection limit expressed as grams per hour can be calculated based on the ΔT and the distance from the OGI camera to the leak location under common leak survey conditions. For the same OGI camera, the detection limit can vary by several orders of magnitude due to ΔT and distance. The present work has demonstrated how different OGI detection limits can be. More importantly, this work has, for the first time, formulated equations that can be used to determine OGI detection limits with a given set of leak detection conditions. Being able to quantify OGI detection limit and understand the variables that dictate the detection limit is a significant advancement. It will help OGI to become accepted as a mature field instrument. The variables characterized in this work should have an impact on the development of OGI leak survey protocols, such as Appendix K to Code of Federal Regulations 40 CFR Part 60 in the United States. Established detection limits will also help emission inventory for fugitive emissions when OGI is used as the sole leak detection method.Implications: Optical gas imaging (OGI) has been used for leak detection and control of fugitive volatile organic compound (VOC) emissions and methane emissions due to equipment leaks. However, detection limits of OGI have not been characterized and quantified like other detection instruments. The lack of well-understood detection limits has hindered broader applications of OGI. The work presented in this paper represents important steps that will enable OGI users and policymakers to establish (1) OGI detection limits under various conditions, (2) OGI leak survey criteria for a desired minimum detectable leak size, and (3) maximum potential emissions from the nondetect sources in emission inventory studies.
A new method has been developed for a direct and remote measurement of industrial flare combustion efficiency (CE). The method is based on a unique hyper-spectral or multi-spectral Infrared (IR) imager which provides a high frame rate, high spectral selectivity and high spatial resolution. The method can be deployed for short-term flare studies or for permanent installation providing real-time continuous flare CE monitoring.In addition to the measurement of CE, the method also provides a measurement for level of smoke in the flare flame regardless of day or night. The measurements of both CE and smoke level provide the flare operator with a real-time tool to achieve "incipient smoke point" and optimize flare performance.The feasibility of this method was first demonstrated in a bench scale test. The method was recently tested on full scale flares along with extractive sampling methods to validate the method. The full scale test included three types of flares -steam assisted, air assisted, and pressure assisted. Thirty-nine test runs were performed covering a CE range of approximately 60-100%. The results from the new method showed a strong agreement with the extractive methods (r 2 =0.9856 and average difference in CE measurement=0.5%). Implications: Because industrial flares are operated in the open atmosphere, direct measurement of flare combustion efficiency (CE) has been a long-standing technological challenge. Currently flare operators do not have feedback in terms of flare CE and smoke level, and it is extremely difficult for them to optimize flare performance and reduce emissions. The new method reported in this paper could provide flare operators with real-time data for CE and smoke level so that flare operations can be optimized. In light of EPA's focus on flare emissions and its new rules to reduce emissions from flares, this policy-relevant development in flare CE monitoring is brought to the attention of both the regulating and regulated communities. PAPER HISTORY
Infrared imager is an efficient tool for detecting gas leaks from process equipment and has been used in leak detection and repair (LDAR) programs for control of fugitive emissions. However, the information regarding which chemical compounds can be imaged and how sensitive a given infrared imager is for various compounds is limited. A theoretical method is presented in this paper that can answer these questions without conducting resource-intensive experiment. The results of this theoretical method has good agreement with experimental data. The method has been used to predict relative sensitivity for 398 compounds.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.