Low-cost Sensor Applications for Improved Control of Fugitive Industrial EmissionsHaley Lane1, Eben Thoma2, Parikshit Deshmukh3, Jacob Cansler*, and Wei Tang4

1 Oak Ridge Institute for Science and Education Researcher with U.S. EPA, ORD, RTP, NC2 U.S. Environmental Protection Agency (EPA), Office of Research and Development (ORD), Center for Environmental Measurement and Modeling (CEMM), RTP, NC3 Jacobs Technology Inc., RTP, NC* formerly Jacobs Technology Inc.4 Applied Research Associates Inc., RTP, NC

Researchers, regulators, and industry all seek better ways to characterizeand manage air pollutant emissions from spatially and temporallycomplex sources. Stochastic emissions from fugitive leaks andmalfunctioning industrial processes can be difficult to identify andmanage. Time-resolved fence line monitoring stations are expensive toimplement, and traditional periodic leak detection and passive samplerfence line approaches carry high temporal latency, limiting responseefficacy. Next generation emissions measurement (NGEM) methods canleverage lower-cost air pollution sensor technologies in conjunction withgeospatial modeling capabilities and data integration concepts to providefast, cost-effective alternatives to conventional approaches. However, newsensor technologies can suffer from baseline drift and other artifacts thatcomplicate sensors’ abilities to provide accurate and actionable data. Todate, sensors such as miniature photoionization

The U.S. EPA Office of Research and Development and the City of LouisvilleMetro Air Pollution Control District are working together to demonstrateemerging NGEM approaches in the industrial region west of Louisville, KY,known as Rubbertown. The area has faced challenges related to ozonecontrol, HAPs exposure, and reoccurring odor issues. While emissions ofmany HAPs have been reduced over the last 15 years, certain toxics, suchas 1,3-butadiene remain a source of concern. Started in September 2017,the Rubbertown NGEM Project is a 2-year field deployment testing varioussensor technologies at 10 primary sites in the Rubbertown region for thepurpose of researching NGEM approaches.1

Disclaimer: This poster has been subjected to review by EPA ORD andapproved for presentation. Approval does not signify that the contents reflectthe views of the Agency, nor does mention of trade names or commercialproducts constitute endorsement or recommendation for use.

CASE STUDY: June 9, 2018

Motivation

detectors (PIDs), show promise in detectingvolatile organic compound (VOC) andhazardous air pollutant (HAP) emissions atlow levels if baseline effects can be controlled.

U.S. EPA’s SPod Sensor• 10.6 eV PID w/heating for baseline

stabilization• High-sensitivity VOC measurements

(~ 10 ppbv)• Coupled wind and atmospheric

measurements• Open-source, solar powered

Rubbertown Project

S(t) = sensor measurement at time t PFT = fast response target signal (sharp peaks) due to nearby stochastic source emissions of interest BST = slow response target signal due to dispersed sources of interest PFNT = fast response non-target signal due to sources not of interest (e.g. truck passing by)

BSNT = slow response non-target signal due to changing airshed VOC levels, not of interest NF = fast response, normally distributed sensor noise and significant artifactsNS = slow response sensor noise due to local conditions (e.g. humidity) and baseline electronic drift

Near-Source, Stationary Leak DetectionA time-resolved, near-source PID sensor signal [𝑆 𝑡 ] is comprised of multiple componentsthat originate from proximate and distant VOC sources (target and non-target) and sensornoise inferences.

𝑆 𝑡 = (𝑃𝐹𝑇+𝐵𝑆𝑇) + 𝑃𝐹𝑁𝑇 + 𝐵𝑆𝑁𝑇 + (𝑁𝐹 + 𝑁𝑆)

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SPod Fence Line Data Processing MethodEPA’s current baseline correction and fence line detection algorithm focuses on peakidentification (PFT+PFNT), rather than integrated signal estimation (which would includeBST+BSNT). It isolates and removes slower components, minimizing drift effects (NS) butpartially removes slower variations in VOC signal.

SPod Data Quality Diurnal Effect Emissions Detection

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Unit j has an ~4x higher responsivity compared to Unit d. However,its NF (72 cts) is ~7x greater, making it more difficult to identifysmaller VOC signals compared to Unit d. Both sensors exhibitapproximately normal noise distributions with artifact removaltypically not required. EPA’s SPod data processing method averagesthe native 1 Hz data to 10 seconds to reduce noise and data densityfor the baseline correction algorithm, without significant loss of PFT

detection capability.

SPod baseline stability was improved over previous deploymentsby incorporating a polyimide strip heater running at ~30°C. Theseimprovements were significant, but baseline variation still showscorrelation with relative humidity (RH), with a diurnal baselinevariation of >50 cts. on June 9th (below). EPA’s baseline-correctionalgorithm does not currently consider temperature or humidity buteffectively captures and reduces the NS component.

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Baseline-corrected data (above)show PFT signal and raw data(right) show PFT + BST signal at4:00-5:00 and 6:00-8:00, withaverage winds speeds <0.5 m/s.The baseline-correction largelyremoves the BST componentpresent in the raw data. TheBST:PFT ratio is typically large in

Sensor Mean Std Dev

Unit d 1,398 cts 11 cts

Unit j 2,099 cts 72 cts

Both the corrected (above) and rawdata (table right) show stablebaselines from 1:00 to 3:00. Prior tobaseline-correction, the raw datademonstrates different raw baselineoffsets and noise levels (NS+ NF).

6/9 1:00 – 3:00 AMRaw 1 Hz PID counts

calm overnight conditions, decreasing as the dayprogresses. In the 12:00-2:00 window, the BST, signalis indiscernible in the raw data, due both toatmospheric transport and source location changes.Modulated daytime PFT signal is typical near sources.

south of Site 8. It was possibleto identify the likely sourcelocation, but this was primarilydue to the lack of potentialsources south of the sensorsite. Typically, it is difficult toidentify source locations underlow wind speed, limiting SPodfence line sensor sourcelocation potential in calmerconditions.

QUIC Modeling: 4:43 AM

Starting at 12:20, a sustained PFT signal indicates a strong nearbyemission source. Wind speeds were higher during this period,and wind was blowing primarily from the west. These repeateddetections can be correlated with wind data to identify likelyplume directions, as shown below in the SDI plot (below left).QUIC modeling helps identified a likely source location within anearby facility (below right). This demonstrates the potential ofcoupled measurement/model NGEM approaches for emissionsdetection and mitigation.1

Source Direction Indicator (SDI) Plot

Illustrates observed concentrations at different wind speeds and directions

Los Alamos QUIC Dispersion Model2

Forward dispersion plume transport with flow obstructions from estimated source location

for selected time period

Remove artifacts; Reduce to 10

second averages

Remove artifacts; Reduce to 10

second averages

Estimate and subtract baseline

Estimate and subtract baseline

Identify emissions

signal detections

Identify emissions

signal detections

Source Identification

• Source direction indicator plots

• Back trajectory modeling

• Source dispersion modeling

Source Identification

• Source direction indicator plots

• Back trajectory modeling

• Source dispersion modeling

This poster is focused on datacollected at Site 8 (map left)from two collocated EPA SPodmonitors testing two differentPID sensor models. Thesesensors collect time-resolved,non-speciated, uncalibratedVOC plume detection signal inmV, digitized to 16 bits, andreported as “counts” (cts).

1 Thoma, Eben, et al. 2019. Rubbertown Next Generation Emissions MeasurementDemonstration Project, Int. J. Environ. Res. Public Health, 16(11), June 8.doi: 10.3390/ijerph16112041

2 Williams, Michael D., Michael J. Brown, Balwinder Singh, and David Boswell. 2004.QUIC-PLUME theory guide, Los Alamos National Laboratory, 43.

S08

Site 8

Site 8

Site 8

Raw Data

Artificial Baseline

Prior to 8:00, Quick Urban & Industrial Complex (QUIC) Dispersion2

modeling indicates a broad, slow moving plume originating from

CEMM Fugitive and Area Source GroupSource and Fenceline Measurements

Methods and Technology Development