Additional Mobile Laboratory Monitoring in San Antonio during the Air Quality

Research Program Field Study in 2017

Final Report

PGA Number: 582-17-71581-12

Prepared for the Texas Commission on Environmental Quality (TCEQ)

Principal Investigators James Flynn, University of Houston

Rob Griffin, Rice University Rebecca Sheesley, Baylor University

Sascha Usenko, Baylor University

Contributors Fangzhou Gou, Rice University Subin Yoon, Baylor University

Matt Erickson, University of Houston Eugenia Velasco, University of Houston Sergio Alvarez, University of Houston

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Executive Summary

The San Antonio area (Bexar County) is currently designated as in attainment of the 2008 National Ambient Air Quality Standards (NAAQS), however Governor Abbot has recommended that Bexar County be designated as nonattainment for the 2015 standard of 70 ppbv. Because this area has been previously been designated as attainment in the past, there have not been scientific field studies of the area. During this project, the University of Houston, Rice University, and Baylor University were contracted to deploy the Mobile Air Quality Lab (MAQL) to Bexar County for more than three weeks during May 2017. This deployment was in conjunction with other field deployments, also funded directly from the TCEQ or through the Air Quality Research Program (AQRP).

The MAQL was deployed to Traveler’s World RV Resort south of downtown San Antonio on May 1st with measurements beginning the following day, more than one week prior to the initially planned deployment date. This early deployment, at no additional cost to the TCEQ, allowed for the characterization of a high ozone event that would have otherwise been missed. Measurements concluded on May 31st. Additionally, the universities leveraged resources to establish a second monitoring site in a parking lot on the University of Texas – San Antonio (UTSA) campus. Logistical support from the TCEQ and Dave Sullivan (University of Texas – Austin) provided power and security for this site. In the final days of the campaign, the MAQL and participants from Aerodyne Research and Drexel University collocated measurements at the UTSA site for instrument intercomparisons and exchange of standards.

Findings from this campaign show that for the modeled periods (99 hours of daytime observations), the ozone photochemistry was nitrogen oxides (NOX)-limited more than 80% of the time. The transition to a NOX-saturated condition occurred at ~1.5 ppbv of nitric oxide (NO). Volatile organic compounds (VOC) measurements by Proton Transfer Reaction–Mass Spectrometry (PTR-MS), Quantum Cascade Laser (QCL), and VOC canister samples reveal that isoprene is the single largest contributor to the overall VOC+˙OH (volatile organic compound + hydroxyl radical) reactivity involved in O3 production, followed by other alkenes as lumped in the model. Reactivity due to ethane is a negligible contributor to the overall reactivity. VOC measurements also allow for identification of anthropogenic, biogenic, and biomass burning influences.

Several periods of high aerosol loadings were observed during the field campaign. These aerosols were dominated by organics, sulfate, and ammonium. Preliminary evaluation indicates that biomass burning in Mexico may contribute to the observed high aerosol loadings. Filter samples collected during the campaign are available for future analysis and may provide additional insights into sources and ages of the observed aerosols in San Antonio.

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Table of Contents 1. Introduction ............................................................................................................... 4

2. Methods..................................................................................................................... 5

2.1. Mobile Air Quality Lab ..................................................................................... 5

2.2. Site description .................................................................................................. 8

3. Results and Analysis ............................................................................................... 13

3.1. Mobile Air Quality Lab (MAQL) .................................................................... 13

3.1.1. QA/QC Summary ......................................................................................... 21

3.2. Proton Transfer Reaction Mass Spectrometer (PTRMS) ................................ 27

3.2.1. Sample conditioning system......................................................................... 27

3.2.2. PTR-MS data ................................................................................................ 30

3.2.3. PTR-MS QA/QC .......................................................................................... 33

3.2.4. PTR-MS issues in the field and corrective actions ...................................... 36

3.2.5. VOCs measured by PTR-MS ....................................................................... 36

3.3. Particle composition and size measurements .................................................. 50

3.4. Planetary Boundary Layer (PBL) .................................................................... 55

3.5. VOC Canister Samples .................................................................................... 56

3.6. Additional Measurements ................................................................................ 59

3.7. Zero-Dimensional Box Modeling .................................................................... 59

3.7.1. Langley 0-D constrained steady state model................................................ 59

3.7.2. VOC lumping ............................................................................................... 60

3.7.3. O3 production rates ....................................................................................... 64

3.7.4. VOC reactivity ............................................................................................. 65

3.7.5. NOX – VOC sensitivity ................................................................................ 67

3.8. Data Archival ................................................................................................... 68

3.9. Presentation of results at national meetings .................................................... 68

4. Conclusion and Recommendations ......................................................................... 69

4.1. Conclusions ..................................................................................................... 69

4.2. Recommendations and future work ................................................................. 70

5. References ............................................................................................................... 72

6. Appendix A ............................................................................................................. 74

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1. Introduction Although the San Antonio area (Bexar County) is currently designated as in attainment of

the 2008 National Ambient Air Quality Standards (NAAQS), the design value for 2013-2015 is listed at 78 ppbv. Governor Abbot has recommended that Bexar County be designated as nonattainment for the 2015 standard of 70 ppbv. Because this area has previously been designated as attainment there have not been scientific field studies of the area. During this project, the University of Houston (UH), Rice University, and Baylor University were contracted to deploy the Mobile Air Quality Lab (MAQL) to Bexar County for approximately three weeks during May 2017.

This deployment was in conjunction with other field deployments, also funded directly from the TCEQ or through the Air Quality Research Program (AQRP). Aerodyne Research, Inc. (ARI) brought its Aerodyne Mobile Laboratory (AML) and a smaller van, the Min-AML, to study O3 photochemistry and precursor emissions in and upwind of the Bexar County area. ARI also brought a support truck equipped to release controlled amounts of gases for use as tracers. Dr. Ezra Wood (Drexel University) was also funded to participate in the field project, measuring the sum of hydro- and organic peroxy radicals (HOx + ROx) and other O3 precursor gases. These measurements can be used to estimate O3 formation rates. Dr. Wood collaborated with ARI and installed his instrumentation into the AML. To allow the ARI labs to more quickly move between sites, UH gave ARI the loan of a 51’ trailer mounted telescoping tower.

In addition to the mobile laboratories, the University of Texas (Dave Sullivan, PI) deployed a radar wind profiler, an acoustic wind profiler, and a ceilometer at the University of Texas – San Antonio site to measure upper air winds and planetary boundary layer heights. An additional acoustic wind profiler was deployed at the Calaveras Lake CAMS station (C59) to provide upwind profiles under prevailing May wind conditions.

UH and St. Edwards collaborated during this same period to launch up to 80 O3 sondes in Bexar County. Fifty of these launches were allocated for May with the remainder to be launched between August and October 2017. Collectively, these field projects presented a significant field team and a comprehensive data set to analyze the causes of high O3 and other air quality issues in the San Antonio area.

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2. Methods 2.1. Mobile Air Quality Lab

The UH-Rice-Baylor MAQL, shown in Figure 1, is comprised of a 325-cubic foot fiberglass truck body in the bed of a 2013 Chevrolet Silverado 3500HD Crew Cab pickup truck. While the volume of the truck body is relatively small, the instrumentation installation was engineered to optimize the use of space and allow for the full suite of measurements to be performed, as evidenced by several years’ worth of data collection in urban Houston.

Figure 1. Photograph of the MAQL in preparation for on-road sampling during DISCOVER-AQ

The truck suspension was converted to an air type suspension to reduce shock and

vibrations that could impact instrument performance. Integrated in the shell are three air-conditioning systems with a total of 38,000 BTU cooling capacity, allowing for operation of instrumentation during summer months. The truck and shell are wired to distribute power from either a generator while in motion or from a 50A recreational vehicle power outlet for stationary measurements. The ambient trace gas sample air is drawn through an inlet box that houses valves, an NOy converter, and power supplies for sampler configuration and calibration. The ambient aerosol is segregated by a 1-µm cyclone inlet and transmitted to the aerosol analytical instrumentation through a 3/8-in. copper tubing inlet.

The trace gas inlet box, aerosol inlet, and meteorological sensors are mounted to the end of a 12-foot articulated arm that allows the MAQL to measure from approximately six feet above

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the ground while in motion and approximately eighteen feet with the arm raised for stationary measurements.

Additionally, the MAQL is equipped with wired and wireless network, dual 4G cellular internet connections, four (front, rear, left, and right) high-definition cameras for identification of emission sources and characterization of local conditions, one hemispheric rooftop camera for cloud condition documentation, perimeter lighting for nighttime operations, and front and rear strobe lights for increased visibility. Other scientists also can monitor the data, instruments, and video outputs remotely in real-time using desktop sharing software.

In situ O3 measurements were collected with a TwoB Technology (Boulder, CO) model 205 dual beam O3 ultraviolet photometric gas analyzer. The nitric oxide (NO) and nitrogen dioxide (NOX) measurements were made with an Air Quality Design (Golden, CO) custom chemiluminescent analyzer, and total active nitrogen (NOy) measurements used a modified Thermo Scientific Model 42C chemiluminescence analyzer. The NOX channel utilizes a blue light converter (BLC) from Air Quality Design (Golden, CO, USA) for photolytic conversion of NO2 to NO. The BLC is more specific in that it essentially converts only NO2 to NO, compared to the standard molybdenum-based NO2-to-NO converter used in most regulatory air monitoring sites that also converts nitric acid (HNO3) and peroxy acyl nitrates to NO and as a consequence tends to overestimate NO2. Measurements of NOy are achieved by placing a Thermo molybdenum (Mo) converter heated to 300°C for conversion of odd nitrogen species to NO at the sample inlet.

Ambient levels of carbon monoxide (CO) were measured with a Los Gatos Research (San Jose, CA) CO Analyzer, model F-CO-23r using laser based off-axis integrated cavity output spectroscopy. Ambient levels of sulfur dioxide (SO2) were monitored using the pulsed ultraviolet fluorescence technique employed in the Thermo Scientific Model 43C-TLE instrument, which takes advantage of the characteristic that SO2 molecules absorb ultraviolet radiation and then fluoresce in the range of 220 to 240 nm. These basic ambient trace level chemical instruments stream data or are queried every second with DAQFactory data acquisition software (Azeotech, Ashland, OR). The trace gas data was post-processed and averaged into a format suitable for storage and use in a database with Igor Pro and MATLAB.

Measurement of VOCs conducted by the BU team used a proton transfer reaction mass spectrometer (PTR-MS) manufactured by IONICON Analytik (Austria) in which target gas molecules are ionized by proton transfer from protonated water. The ionized material is then detected and quantified using a time-of-flight mass spectrometer. The PTR-MS is a form of chemical ionization mass spectrometry. Data was collected with approximately 60s scan times, but these data were averaged to longer time scales, typically 5 and 10 minutes, to match the output from other instruments and to improve detection limits. A “cold trap” sample conditioner, described later, was built for this project to reduce water content in the sample to a -30˚C dew point, allowing for HCHO quantification.

Whole-air samples were also collected using polished SUMMA canisters. These canisters were provided and analyzed by the University of Nevada at Reno’s Desert Research Institute. Data from these cans were used to calculate ratios of gases to those measured by the PTR-MS. These ratios were then applied to the continuous PTR-MS data to allow for a more complete VOC suite for the photochemical modeling. More information on the canister samples and subsequent modeling is described later in this document.

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An ARI Quantum Cascade Laser (QCL) instrument tuned for methane and ethane was installed in a small sampling trailer at the front of the MAQL. This data was used to evaluate the potential impact of upwind oil and gas activities.

Figure 2. ARI QCL instrument installation in small sampling trailer collocated with MAQL.

The Rice team performed measurements of size-resolved aerosol chemical composition at high time resolution (10 minutes) using an Aerodyne (Billerica, MA) high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS). In this instrument, sub-micron particles are classified by size according to their speed within a vacuum chamber, which will allow estimation of their vacuum aerodynamic diameter. Non-refractory material is vaporized, ionized by electron impact, and detected using a time-of-flight mass spectrometer. The HR-ToF-AMS provides chemical speciation for ammonium, nitrate, chloride, sulfate, and several classes of organic matter.

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2.2. Site description In early January 2017 the site visits to San Antonio were conducted to scout potential

locations for the MAQL and ARI labs. The TCEQ windrose for San Antonio in May (Figure 3) show the winds are predominantly from the southeast. In the San Antonio there are several well-equipped CAMS stations, namely C59 (Calavaras Lake), C23 (San Antonio Northwest), and C58 (Camp Bullis). These three sites are generally aligned along a southeast-northwest line which passes over the urban core of San Antonio. Through discussions with the TCEQ it was decided to locate the MAQL at an RV park south of downtown San Antonio (Traveler’s World RV Resort) and to set up a second monitoring site at the UTSA campus on the northwest side of town (Figure 4).

Within Traveler’s World, the MAQL was set-up in a section of the property which primarily housed long-term residents and with as little potential interference from trees or power lines as possible. By staying in the long-term area the MAQL was less likely to be impacted by emissions from short term travelers. This location also allowed the crew to meet neighbors who helped a watchful eye on the MAQL. This site backed up to a park area along the Riverwalk where most early morning and some afternoon ozonesodnes were launched under a separate project. Due to the proximity of the park, it was possible to use long extension cords to provide power for the sonde launches from the MAQL power post. The site also provided a location to store supplies needed for the sonde launches.

The UTSA site was located in a relatively unused parking lot on the southwest corner of campus. Through significant efforts of the TCEQ and UT (Dave Sullivan) arrangements were made to provide power for the ARI and Baylor labs as well as the UT equipment at this location. In the final days of the campaign, the groups were able to arrange the labs within the fencing to conduct an intercomparison and calibration standard exchange (Figure 6).

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Figure 3. TCEQ wind rose for San Antonio for the month of May (1984-1992).

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Figure 4. Google Earth view of the San Antonio area showing the relative locations of Traveler’s World RV Resort, UTSA, and three key TCEQ monitoring sites, C23, C58, and C59.

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Figure 5. MAQL and small trailer with the ARI QCL instrument at Traveler’s World RV Resort.

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Figure 6. UTSA site during intercomparison period at the end of the field campaign showing the UH tower, ARI AML, ARI Min-AML, Baylor trailer, and MAQL (left to right).

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3. Results and Analysis 3.1. Mobile Air Quality Lab (MAQL)

The following figures (Figure 7Figure 9) show an overview of the key measurements made at the MAQL and UTSA sites. In general, although the sites were separated by a significant distance, the trace gases track quite well and show similar patterns. Measurements at Traveler’s World began on the evening of May 1st, the day of arrival, since most of the instruments were powered on before leaving Houston. Since the UTSA measurement trailer needed to be set-up in the field, measurements there did not begin until May 6th. The early deployment to the field meant the teams were in position to capture one of the highest O3 events during May which occurred prior to the initial proposed deployment date of May 10th.

Figure 7. Overview of O3, CO, and SO2 data measured by the MAQL at Traveler’s World and the UTSA site.

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Figure 8. Overview of NO, NO2, and NOy data measured by the MAQL at Traveler’s World and the UTSA site.

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Figure 9. Overview of meteorological parameters measured by the MAQL.

Comparisons of the MAQL, UTSA, and three TCEQ sites for the entire campaign show that the measurements are generally in good agreement. Figure 10 shows a time series of O3 measurements during the campaign for these sites. During the daytime most sites are similar, with more variability at night. NO2 shows more variability as would be expected with a pollutant that is tied more closely with primary emissions of NO titrating ambient O3, however when considering Ox, the sum of O3 and NO2, the differences are reduced. Figure 11 shows a matrix of scatter plots with bivariate fits and correlations for the MAQL O3, NO2, and Ox to the same sites in Figure 9, with the addition of C678, another monitoring site close to the MAQL. Unfortunately, NO2 measurements are not available at C678; therefore, we cannot compare NO2 or Ox for this location.

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Figure 10. Time series comparison of O3, NO2, and Ox (O3+NO2) for the MAQL, UTSA site, and three CAMS sites.

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Figure 11. Scatter plot comparison of O3, NO2, and Ox (O3+NO2) for the MAQL, UTSA site, and three CAMS sites. O3 compares well across the sites, however r2 generally improves when considering Ox to minimize differences from titration due to NO.

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Figure 12. Time series of SO2 and CO for the MAQL, UTSA site, and C59 (SO2 only).

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Figure 13. Time series of wind speed and direction for MAQL and UTSA sites. Generally, there is good agreement for most periods, however there are some differenced in wind direction, particularly at low wind speeds. This will be further investigated in continuing work.

Examining only the intercomparison period where the MAQL was collocated with the Baylor trailer at the UTSA site, the correlations improve significantly for most compounds (Figure 14). The largest differences (greater than 10%) are seen in CO, NO, and SO2, however the correlation is still quite good. Further examination of these data and calibrations for each site will be ongoing. It should be noted that CO and NO were measured with different manufacturer’s instruments, with the MAQL instruments generally being of a higher quality research grade instrument. An off-axis cavity ring-down method was used for CO in the MAQL while a more traditional NDIR method (Thermo 48i-TLE) was used at the UTSA site. In the MAQL, a custom two-channel NO/ NOX chemiluminescence instrument was used, and a Thermo 42i-TL was used. Section 0 below describes the calibration results, measurement uncertainties, and detection limits in more detail.

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Figure 14. Comparison of MAQL vs UTSA measurements during the three-day intercomparison period at the end of the campaign. Agreement is quite good for most species, although some instrument differences are apparent.

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3.1.1. QA/QC Summary The trace gas instruments in the MAQL and at UTSA were routinely calibrated in three

ways: multi-point, span, and NO2 multi-point calibrations. The multi-points and spans were conducted using gas NIST traceable standards from Scott-Marrin for both sites. Zero air was created by Thermo 111 zero air generators using activated charcoal, Purafil SP, and a catalytic oven to remove NO, NO2, SO2, O3, CO, CH4, and other VOCs. The purpose of the multi-point calibrations is to compare the raw output of the instrument to a known quantity and compute a correction factor for the instrument. Multi-points were performed by generating 5 different mixing ratios by holding the zero air flow constant and varying the calibration gas flow followed by a zeroing period. Ozone calibrations were performed with a Thermo 49c-PS which is routinely verified against an EPC standard reference photometer. A multi-point calibration was nominally performed every 4 days at 2 am CST. The resulting responses from each instrument are plotted against the actual mixing ratio generated and applied with a linear fit to produce a correction slope.

Spans are used to monitor changes between multi-points and identify potential issues rather than determining correction factors. Spans were performed on days between multi-point calibrations and consisted of one upscale point followed by a zero. A total of 21 spans were performed on the MAQL and 7 at UTSA.

Table 1 and

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Table 2 detail the correction slopes from the multi-point calibrations for all the trace gas measurements for MAQL and UTSA. For all the slopes, one standard deviation was less than 3% of the average. Table 1 and also show statistics for the spans which are similar to those found with the multi-points.

NO2 multi-point calibrations followed the same procedure as the multi-points but used a different Scott-Marrin gas standard. These calibrations were used to determine the conversion efficiency (CE) of NO2 into NO for the photolytic converter utilized in the NOX instruments at both sites. Table 3 shows the resulting CEs for both sites. Table 1. MAQL correction slopes resulting from periodic multi-point calibrations.

Day O3 CO NOy SO2 NO NOX 2 1.003 1.01 1.292 1.321 - 0.000903 5 - 1.02 1.201 1.326 0.000888 0.000889 9 0.995 1.019 1.228 1.329 0.000879 0.000884 13 0.994 1.013 1.217 1.317 0.000921 0.000947 17 - 1.011 1.231 1.318 0.000915 0.000932 21 0.998 1.011 1.21 1.305 0.000905 0.00093 25 0.996 1.013 1.218 1.355 0.000898 0.000939 29 - 1.013 1.204 - 0.000903 -

Multi Average 0.997 1.014 1.225 1.324 0.000901 0.000918 Std. Dev 0.004 0.004 0.029 0.016 0.000015 0.000025 % Diff 0.36% 0.37% 2.37% 1.17% 1.62% 2.76%

Spans Average - 1.025 1.231 1.329 0.000909 0.000917 Std. Dev - 0.004 0.015 0.019 0.000035 0.000028 % Diff - 0.36% 1.21% 1.41% 3.80% 3.06%

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Table 2. UTSA correction slopes resulting from periodic multi-point calibrations. Day O3 CO NOy SO2 NO NOX 1.011 1.822 1.078 1.561 1.106 1.003 0.979 1.694 1.147 1.515 1.048 16 1.005 0.981 1.806 1.076 1.515 1.048 19 1.007 0.944 1.729 1.094 1.895 1.069 20 1.006 0.977 1.744 1.066 1.834 1.059 22 1.008 0.97 1.726 1.059 1.771 1.025 24 1.002 0.981 1.717 1.091 1.785 1.013

Multi Average 1.006 0.972 1.748 1.087 1.530 1.053 Std. Dev 0.003 0.013 0.044 0.027 0.027 0.028 % Diff 0.28% 1.34% 2.52% 2.48% 1.74% 2.67% Average - - - - 1.821 - Std. Dev - - - - 0.056 - % Diff - - - - 3.08% -

Span Average 1.006 1.002 1.735 1.080 1.705 1.050 Std. Dev 0.001 0.009 0.020 0.014 0.087 0.022 % Diff 0.13% 0.94% 1.16% 1.33% 5.08% 2.10%

Table 3. MAQL and UTSA NO2 conversion efficiencies of the blue light converter used in the AQD NOX instrument resulting from periodic multi-point calibrations with NO2.

MAQL UTSA

Day NO2 CE Day NO2 CE

3 0.853 0.857 11 0.847 16 0.828 23 0.879 20 0.813 29 0.867 22 0.806

Average 0.862 0.826 Std. Dev 0.014 0.020 % Diff 1.67% 2.37%

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The detection limits for the trace gas instruments was characterized as 3 standard deviations away from the noise. Table 4 details the lower limit of detection (LLOD) for the trace gases for MAQL and UTSA. The detection limit for CO was not calculated as the signal was well above the manufacturer detection limits, less than one ppbv for the MAQL and 40 ppbv for UTSA. Table 4 lists the LLOD for the trace gas instruments at MAQL and UTSA.

Table 4. Lower limit of detection (LLOD) of measurement techniques for MAQL and UTSA

LLOD (ppbv)

MAQL UTSA

O3 1.33 0.22 NO 0.01 0.15 NO2 0.013 0.2 NOy 0.06 0.09 SO2 0.12 0.12

Measurement uncertainty is calculated by characterizing all the sources of error and taking

the square root of the sum of the squares.

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Table 5 details the uncertainty of each error source and the total combined uncertainty of the trace gas instruments for MAQL and UTSA. The manufacturers provide uncertainties for mass flow controllers and gas standards; calibration errors and conversion efficiency errors are determined by the standard deviation of calibrations relative to the average of the calibrations.

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Table 5. Uncertainty of measurement techniques for MAQL and UTSA, in percent.

Mass flow controllers

Zero Cal Standard

Cal variation

NO2 standard NOX CE Uncertainty

MAQL O3 2 2 2 0.37

3.5

NO 2 2 2 1.62

3.8

NO2 2 2 2 2.76 5 3.82 1.67 7.9

NOy 2 2 2 2.31

4.2

CO 2 2 2 0.37

3.5

SO2 2 2 2 1.17

3.7

UTSA O3 2 2 2 0.3

3.5

NO 2 2 2 3.1

4.6

NO2 2 2 2 2.0 5 4.65 2.7 8.4

NOy 2 2 2 2.7

4.4

CO 2 2 2 1.5

3.8

SO2 2 2 2 1.3

3.7

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3.2. Proton Transfer Reaction Mass Spectrometer (PTRMS) 3.2.1. Sample conditioning system The sensitivity of the instrument was improved by conditioning or drying the sample air

before it enters the PTR-MS. The air was dried at -30 °C using a cold trap, where water vapor was forced to condense and freeze out of the sample air. The reduction of water vapor allowed for the PTR-MS to operate at a lower Townsend (Td), which resulted in an overall increase in instrument sensitivity. The Td number is the ratio of E/N, where E is the electric field and N is number density of air. One Td is equal to 1E-17 V/Cm2. A decrease in the Td number will increase the overall reaction time within the drift tube and thereby increase the instruments sensitivity. This is especially important for formaldehyde which has only a slightly higher proton affinity than water. In addition, the ions are traveling slower through the drift tube under lower Td, which results in less kinetic energy. The kinetic energy of the ions can be a cause fragmentation when ions collide, so lowering the kinetic energy reduces fragmentation.

The condensed water vapor produces a quasi-liquid water layer on the surface of the ice

at temperatures as low as -25 C. Formaldehyde has a very high water solubility and will partition (Henry’s Law Constant = ~3E5 M/atm) to this liquid water layer. To correct for this and to allow for continuous measurement the conditioning system was equipped with two drying tubes (A and B tubes) that were controlled to -30 C during sampling to avoid formation of the quasi-liquid layer. Dry tube A and B consisted of a stainless-steel tube coated with amorphous silicon (1/4 inch tube, 150 mm in length). Tubes A and B were secured within an aluminum block cooled using an immersion cooler probe. The temperatures inside each tube was regulated using independent heating wires and thermocouples. The tubes were conditioned for formaldehyde prior to operation using ambient air. For example, when tube A was in operation, tube B would be cooled to -30 °C and conditioned with ambient water vapor and formaldehyde for 25 min. During consecutive use, each tube would be warmed (~40 °C for 10 min) after operation and between conditioning to remove the excess water that accumulated during operations.

The sampling conditioning system was comprised of two main component boxes and an

immersion cooler with probe. The overall system was divided between two boxes. The fluids box controlled the flow of air through the cold trap and was outfitted with an aluminum block (cold trap), air pump, palladium catalyst (zero air generator), and nine 24 V 3-way PTFE isolation values (Figure 15). The second box was an electric control box outfitted with a power supply, solid state relays, temperature controllers, and computer interface (Figure 16).

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Figure 15. Conditioning and calibration system: cold trap and fluid box. Fluid box includes aluminum block (cold trap), air pump, palladium catalyst (zero air generator), and nine 24 V 3-

way PTFE isolation values.

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Figure 16. Conditioning system electrical box outfitted with a power supply, solid state relays, temperature controllers, and computer interface.

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3.2.2. PTR-MS data The raw signal from the PTR-MS is converted into a mixing ratio (ppbv) through a

number of steps. First, the signal [Hz] for the VOC of interest, RH+, is normalized to H3O+ (MHz at m/z 21) to get a normalized signal [RH+ Hz/MHz m/z 21]. Second, the normalized signal is subtracted by the normalized background [RH+ Hz/MHz m/z 21] which is the m/z signal (Hz) corresponding to the RH+ measured during the periodic zeros (i.e. zero air). Third, the background corrected normalized signal is divided by the sensitivity [RH+ Hz/(MHz m/z 21*ppbv)] to result in a mixing ratio [ppbv]. The sensitivities are calculated from the periodic calibrations. Data of all species measured by the PTR-MS is presented in 5-min averages.

Converting the raw signal of formaldehyde (m/z 31) into a mixing ratio was performed in

the same steps as the other signals but required additional QAQC procedures. Instances that resulted in formaldehyde failing a QAQC check included: conditioning tube temperatures variable or above recommended threshold, differences in conditioning and temperatures between the tubes, and unstable diagnostic ions generating fluctuations in the background and/or sensitivity.

An additional correction was applied to formaldehyde based on comparisons to the

Aerodyne QCL formaldehyde measurements when they were collocated at UTSA from the 27th through 31st. Figure 17 shows the resulting comparison. The PTR-MS seems to be about a factor of 2.5 above the Aerodyne QCL. The likely cause of the difference is with the calibration and sensitivity calculation. Due to some of the formaldehyde QAQC checks listed above, it is possible that the sensitivity is underestimated. Therefore, the sensitivity was increased by a factor of 2.5 to compensate for this difference and a fit of the corrected PTR-MS data compared to the Aerodyne QCL can be observed in Figure 17. Further laboratory testing and calibrations are required to resolve this difference.

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Figure 17. Comparison of formaldehyde measurements between the Baylor PTR-MS and the Aerodyne QCL

After correction, the PTR-MS HCHO data compares quite well with both the Aerodyne QCL and AeroLaser Hantzch reaction HCHO measurement which was operated at the UTSA site for much of the campaign (Figure 18). Figure 19 shows that as expected, after the correction the PTR-MS HCHO agrees quite well with the Aerodyne measurements, however there is significantly more noise in the PTR data. The AeroLaser instrument in the UTSA trailer also agreed with the Aerodyne data within 7% and an r2 of 0.96.

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Figure 18. Time series of corrected PTR-MS HCHO after correction compared to AeroLaser and Aerodyne HCHO during the intercomparison period at UTSA

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Figure 19. Scatter plot of agreement between PTR-MS and AeroLaser HCHO with the Aerodyne QCL measurements for the intercomparison period.

3.2.3. PTR-MS QA/QC Daily zeros, calibrations, and checks of the instrument and the conditioning box were

done by Baylor University’s graduate student, Subin Yoon, and University of Houston’s research scientist, Matthew Erickson, at both sampling sites for quality control and quality assurance (QA/QC) purposes.

For the PTR-MS, checks on the instrument were the vacuum pressures (i.e. PC pressure and P-drift pressure) and the ion source (H30+ count, H2O clusters, etc.) which were checked and recorded daily. If values were not within range or unstable, tunings were done in order to get the instrument back to its optimal state. Pressure checks of the gas cylinders (i.e. calibration gas, formaldehyde, and nitrogen) enabled early detection of when cylinders were needed to be replaced. Nitrogen gas cylinders were replaced most frequently. Other gas cylinders were checked to ensure the pressure control valves were set to their correct values.

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Single-point calibrations and zeros were scheduled to run from 02:00 to 03:00 daily. Zero air was generated by passing lab air through a heated catalyst (alumina on palladium at 350 C). Several multi-point calibrations were also completed during this campaign including May 4th, 9th, 22nd, 29th, and 31st. The calibration gases included the following species: acetonitrile (m/z 42), acetaldehyde (m/z 45), acetone (m/z 59), dimethylsulfide (m/z 63), isoprene (m/z 69), methanol (m/z 33), methyl vinyl ketone (MVK) and methacrolein (m/z 71), methyl ethyl ketone (MEK) (m/z 73), benzene (m/z 79), toluene (m/z 93), and camphene (m/z 137). A separate formaldehyde (m/z 31) calibration gas tank was also included in the system. Additional species were quantified using University of Minnesota’s calibration gas tank which also includes hydroxyacetone (m/z 75), xylenes and ethyl benzene (C2-benzenes) (m/z 107), C3-benzenes (m/z 121) and C4-benzenes (m/z 135). Calibrations and zeros were routinely analyzed for consistency and accuracy to ensure that the PTR-MS and calibration systems were operating properly.

Field-based limits of detection (FLOD) were characterized as 3 standard deviations above the ambient background during the San Antonio Field Study (SAFS). The standard deviations were determined by grouping the signals from the zeros performed during the project, subtracting the average background from the signals, and calculating the standard deviation from these data. The standard deviation was then converted into a mixing ratio by applying the sensitivity. Table 6 shows the FLOD for species of interest. This is biased high for compounds consistently present in the ambient atmosphere.

Uncertainties were calculated through identifying all the sources of error then taking the square root of the sum of the squares of the sources. The sources of error were determined to be the calibration and zero mass flow controllers (2%), the calibration standard (5%), error associated with sensitivity (1.5% to 9%), and instrument noise (4% to 8%).

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Table 6. List of 5-min FLOD and uncertainty for all reported VOC data.

VOC m/z 5-min FLOD (ppbv)

Uncertainty (%)

Formaldehyde 31 1.74* 15%*

acetonitrile 42 0.16 8.5

acetaldehyde 45 0.19 7.2

acetone 59 0.16 7.4

dimethyl sulfide 63 0.56 9.0

isoprene 69 0.29 9.9

MVK and methacrolein 71 0.28 12.5

MEK 73 0.22 9.2

hydroxyacetone 75 0.13 9.8

benzene 79 0.26 9.3

toluene 93 0.29 9.3

styrene 105 0.39 10.5

xylenes and ethyl benzene (C2-benzenes)

107 0.46 10.2

C3-benzenes 121 0.51 10.4

C4-benzenes 135 0.54 10.6

∑monoterpenes 137 0.54 10.5

* Estimated preliminary values Using Team Viewer, remote access to the PTR-MS computer and controls of the

conditioning box allowed regular check-ins throughout all hours to confirm proper functioning of the instruments. Warning emails were also set up to notify key individuals of system failures including shut down or restarts of the PTR-MS computer, errors with automated program for the conditioning box, etc.

Preliminary data work-up and plots of interested VOC species were regularly drafted and shared amongst the group for discussion and potentially to present during daily phone conferences. For this report, select data have been plotted. Final data will be posted to the archive for TCEQ viewing.

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3.2.4. PTR-MS issues in the field and corrective actions During May 3rd to May 8th, due to issues with the mass calibration, only certain VOCs

(acetonitrile, acetaldehyde, acetone, isoprene, MVK and methacrolein, and MEK) were quantified. It is suspected that during the initial starting of the instrument at Traveler’s World, the PTR-MS did not properly read a configuration file which is used to correct mass identifications in the mass spectrometer. The issue was not identified in the first days of the campaign with the increased workload of site set-up, however once identified the issue was quickly resolved before the original deployment date of May 10.

There were also unexpected shutdowns and restarts of the PTR-MS on certain days (May 3rd, May 4th, May 20th, May 27th, May 29th) which caused several hours of data loss. Due to these incidents, email alerts were set-up when the PTR-MS would restart/shutdown itself. The cause of this issue was the PTR-MS computer itself, which will need to be replaced prior to deployment on another field campaign.

Starting May 22nd, there were issues with the sample conditioning system. The immersion cooler was no longer efficiently cooling the aluminum block. In the end, the issues with the system were found to be due to the orientation of the immersion probe, which had caused pooling of oils in certain areas of the probe. Immediate fixes were made including draining the probe and extending the conditioning times to allow longer cooling time for tubes A and B. Measurements are reportable when the aluminum block was in correct temperature ranges which was mainly during cooler times of day. This primarily impacted formaldehyde. A new immersion cooler was received and installed May 25th. The conditioning box will need to be reoriented so this will not be an issue in the future. Even with these incidents, 86% of data was reported during the contracted period.

3.2.5. VOCs measured by PTR-MS Direct real-time measurements of VOCs can be used to infer sources of urban ozone as

VOCs and NOX serve as two of the main precursors of ozone. Specific VOC can serve as anthropogenic (AVOC), biogenic (BVOC), or biomass burning (BBVOC) tracers. Examples of AVOCs, BVOCs, and BBVOCs including toluene and benzene, isoprene and monoterpenes, and acetyldehyde and acetonitrile, respectively. It is acknowledged that acetaldehyde is not a unique tracer for BBVOC; for SAFS it will be used in combination with other tracers to confirm BB events. To visualize the trends in AVOC, BVOC and BBVOC over the course of SAFS, three tracers have been plotted in Figure 20 and Figure 21. AVOC as traced by benzene was relatively consistent. There was a prominent BBVOC event in the first week of the campaign (5/4–5/7), which will be discussed in more detail. BVOC as traced by isoprene also peaked in that first week, nearly concurrent with the BBVOC event.

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Figure 20. Time series of an AVOC (benzene), BVOC (isoprene), and BBVOC (acetyldehyde) collected at San Antonio’s Traveler’s World (TW) field site.

Figure 21. Time series of an AVOC (benzene), BVOC (isoprene), and BBVOC (acetyldehyde) collected at University of Texas San Antonio (UTSA) field site.

Automobile traffic has been shown to produced toluene to benzene (T/B) ratios of 1.3 to 4.0. Other potential sources of AVOCs include oil and natural gas production areas including oil wells (T/B ≈ 2.8), natural gas wells (T/B ≈ 2.0), compressor station (T/B ≈ 1.5), fracking wells (T/B ≈ 4.0). Toluene has a much shorter atmospheric lifetime against ˙OH radical than benzene (2.4 vs 12 days). Our time series in Figure 22 and Figure 23 show higher mixing ratios of

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benzene and toluene at night (20:00 to 07:00). Toluene and benzene had significant scatter in the correlation at TW during rush hour (05:00 – 09:00, r2 = 0.17, slope of 1.5; average ratio 2.4 ± 2.3), but had higher correlation in the nighttime (20:00 – 7:00, r2 = 0.30, slope of 2.66). At TW, the rush hour slopes for toluene:benzene and the average ratios are both in the range for traffic and other fossil sources.

Figure 22. Time series of benzene (AVOC) measured at San Antonio’s Traveler’s World (TW: top) and University of Texas San Antonio (UTSA: BOTTOM) field sites.

For UTSA, the dataset is significantly smaller, however, the correlation for

toluene:benzene is better for rush hour (05:00 – 09:00, r2 = 0.38, slope of 0.89; average ratio 1.44 ± 0.87) and similar to TW at night (20:00 – 07:00, r2 = 0.36, slope of 0.76). The slopes and average ratio for UTSA is for toluene:benzene is lower than TW. This could indicate a more aged signal at UTSA or may indicate an additional source of toluene near the TW site (Figure 23).

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Figure 23. Time series of toluene (AVOC) measured at San Antonio’s Traveler’s World (TW: top) and University of Texas San Antonio (UTSA: BOTTOM) field sites.

For the biogenic emissions, it is maybe helpful to understand the local and regional species that are in and around San Antonio. Based on the Texas Parks and Wildlife Department Ecosystem Map (https://tpwd.texas.gov/landwater/land/programs/landscape-ecology/team/ ), there are several local vegetation types and physiognomic regions. To fully investigate differences based on these vegetation types, it would be conducive to monitor over an annual cycle to better account for seasonal impacts and to have longer time series from different source regions.

Within San Antonio, there are several urban vegetation types which are not necessarily native. These were closest to the Traveler’s World site. This also can include grassland and woodland types. The woodland type is a Native Invasive: Deciduous Woodland. This broadly-defined type is mapped on prairie soils from the Blacklands Prairie region westward. These areas have often been heavily grazed, formerly plowed, or fire suppressed. Common species across all regions may include mesquite, sugar or netleaf hackberry, and cedar elm. Juniper species are often a component. To the west, species such as Siberian elm and western soapberry are

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common. https://tpwd.texas.gov/landwater/land/programs/landscape-ecology/team/ The grassland Blackland Prairie, as a disturbance or tame grassland. “This type includes grasslands in many conditions, and introduced grasses such as Bermudagrass and King Ranch bluestem are the most frequent dominant species. Shrubs or tress such as mesquite, cedar elm, eastern redcedar, surgar hackberry, and huisache may be present, but typically have low cover.” https://tpwd.texas.gov/landwater/land/programs/landscape-ecology/team/

Northwest of the UTSA site is the Edwards Plateau, which can include both woodland and grassland zones. The woodland zone is described as Ashe Juniper Motte and Woodland. “Ashe juniper and plateau live oak are the most frequent dominants of this evergreen woodland. Some areas are characterized by nearly pure stands of Ashe juniper, while others have taller plateau live oaks with an understory of smaller Ashe juniper. Lacey oak and papershell pinyon may be important to the west, and white shin oak in the central and eastern part of the range. Persimmon and agarito are common shrubs.” https://tpwd.texas.gov/landwater/land/programs/landscape-ecology/team/ The grassland zone is called Savanna Grassland. “Grassland condition varies for this mapped type, but many areas contain non-native King Ranch bluestem as an important species, and Bermudagrass is also frequent. Common native grasses include little bluestem, sideoats grama, silver bluestem, Texas wintergrass, purple three-awn, and common curlymesquite. Trees and shrubs are usually present, and may include plateau live oak, Ashe juniper, mesquite, agarito, and/or cedar elm.” https://tpwd.texas.gov/landwater/land/programs/landscape-ecology/team/

To the south and east of San Antonio, the native vegetation is a Post Oak Savannah. “A variety of grasslands are circumscribed within this type, and disturbance or tame grasses such as Bermudagrass, King Ranch bluestem, kleingrass and bahiagrass (east) are common dominants. Little bluestem, Indiangrass, silver bluestem, Texas wintergrass, tall dropseed, and brownseed paspalum are native species that may be important. Common broomweed, western ragweed, and hog croton are common weedy herbaceous species. Post oak, mesquite, eastern redcedar, blackjack oak, water oak, and yaupon are common woody species and may form sparse woodlands or shrublands in some areas.” https://tpwd.texas.gov/landwater/land/programs/landscape-ecology/team/

To trace the potential impact of biogenic VOC sources (BVOC) on the urban VOC in San Antonio, two sets of BVOCs were measured (Figure 24-

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Figure 27). Isoprene and monoterpene emissions will vary by plant species. However, they are also emitted by different mechanisms. Isoprene is a byproduct of photosynthesis and/or photorespiration; it is not stored in the plant and will have a strong dependence on sunlight and temperature (Seinfeld and Pandis, 2016). This is why typical plots of isoprene mixing ratios display a strong diel cycle, with high mixing ratios in the daytime and very low mixing ratios at night. Monoterpenes are present in the leaf oils and resins, have no dependence on sunlight, and smaller response than isoprene to temperature (Seinfeld and Pandis, 2016). Within the SAFS campaign, isoprene had very strong diel trends (Figure 24 Figure 25), as expected, while monoterpenes had much lower mixing ratios at both sites and no diel trend (

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Figure 26 and

Figure 27). Roughly, the BVOC mixing ratios for both isoprene and monoterpenes was lower than measured in Houston in 2016 using the same PTRMS in the MAQL (Jones Forest had a daily max at 2.5 ppbv for isoprene and 2 ppbv for monoterpenes). The isoprene mid-day average (12:00 – 17:00) was 0.80 ± 0.59 ppbv for TW and was 1.8 ± 1.1 ppbv for UTSA. The monoterpene average was 0.21 ± 0.15 for TW and 0.21 ± 0.15 for UTSA. This indicates that the monoterpene source was consistent across San Antonio, while the isoprene source had significant spatio-temporal differences.

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Figure 24. Time series of isoprene (BVOC) measured at San Antonio’s Traveler’s World (TW: top) and University of Texas San Antonio (UTSA: bottom) field sites.

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Figure 25. Average hourly isoprene (BVOC) concentrations (ppbv) at San Antonio’s Traveler’s World (TW: top) and University of Texas San Antonio (UTSA: bottom) field sites.

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Figure 26. Time series of monoterpenes (BVOC) measured at San Antonio’s Traveler’s World (TW: top) and University of Texas San Antonio (UTSA: bottom) field sites.

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Figure 27. Average hourly monoterpenes (BVOC) concentrations (ppbv) at San Antonio’s Traveler’s World (TW: top) and University of Texas San Antonio (UTSA: bottom) field sites. To further investigate potential BBVOC trends during SAFS, acetaldehyde and acetonitrile were measured, with the observed trends described below (

Figure 28-Figure 30). Both laboratory and field studies have used acetonitrile (CH3CN) and acetaldehyde (CH3COH) as tracers for biomass burning (Holzinger, et al., 1999; Holzinger, et al., 2005; Karl, et al., 2007). Acetonitrile is primarily emitted from burning of vegetation and is not produced from gas phase chemistry in the atmosphere. Photochemical lifetime of acetonitrile is estimated to be approximately 2 years (Holzinger, et al., 2005; Karl, et al., 2007) while atmospheric lifetime was measured to be around 6 months (Holzinger, et al., 2005). The likely reason for this shorter lifetime is from deposition over land and/or sea (Sanhueza, et al., 2004) relative to photochemical degradation. Acetaldehyde has a photochemical lifetime is 11.5hrs

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(Laat, et al., 2001) while atmospheric lifetime is 3.3hrs (Seinfeld and Pandis, 2016), much shorter than acetonitrile. Sources of acetaldehyde can also be from motor vehicular emissions (Satsumabayashi, et al. 1995; Anderson, et al., 1996), decaying plant matter (Warneke, et al., 1999) and also secondary products of photochemical oxidation of anthropogenic hydrocarbons (Satsumabayashi, et al. 1995).

Earlier in the month (May 2nd - 9th), when measurements were made at Traveler’s World, there were elevated concentrations of both acetonitrile (avg: 0.29 ± 0.12 ppbv) and acetaldehyde (avg: 1.86 ± 1.43 ppbv) which was concurrent to agricultural burnings in Mexico taking place near the Gulf. Strong correlations (r2 0.77) were observed of these two tracers during this biomass burning event. This data is in red in Figure 30. The data excludes measurements during rush hour, which had a weaker correlation of (r2 0.51) likely due to atmospheric processing of the tracers.

There is no correlation between acetonitrile and acetaldehyde when data from the non-biomass burning period at Traveler’s World (i.e. May 10th – 27th) is included (Figure 14). Concentrations of acetonitrile and acetaldehyde were lower during non-biomass burning events at both Traveler’s World and UTSA site (May 27th – 31st). Average concentrations of acetonitrile and acetaldehyde at Traveler’s World are 0.20 ± 0.07 and 1.29 ± 0.73 ppbv, respectively. At the UTSA site average concentrations of acetonitrile and acetaldehyde are 0.21 ± 0.08 and 0.94 ± 0.45 ppbv, respectively. For the non-biomass burning periods, concentrations of acetonitrile were similar at both sites while concentrations of acetaldehyde were more enhanced at Traveler’s World site. This may be due to a more local source including vehicle emission or can be generated as a photo oxidation by-product.

During the biomass burning period, diurnal patterns for both tracers are generally higher during the night time, 20:00 to 07:00, and lower during the day, 07:00 to 20:00. This was strongly observed in the acetaldehyde data. No distinct diurnal pattern was observed during the non-biomass burning period for both tracers.

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Figure 28. Time series of acetyldehyde (BBVOC) measured at San Antonio’s Traveler’s World (TW: top) and University of Texas San Antonio (UTSA: bottom) field sites.

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Figure 29. Time series of acetonitrile (BBVOC) measured at San Antonio’s Traveler’s World (TW: top) and University of Texas San Antonio (UTSA: BOTTOM) field sites.

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Figure 30. Correlation of acetonitrile (BBVOC) and acetaldehyde measured at San Antonio’s Traveler’s World. The red points indicate measurement during a suspected biomass burning event period from May 2 – May 9.

3.3. Particle composition and size measurements Non-refractory submicron particulate matter (NR-PM1) was collected during the

campaign using an Aerodyne High-Resolution Time-of-Flight Mass Spectrometer (HR-ToF-AMS). The HR-ToF-AMS provides concentration of chloride, nitrate, sulfate, ammonium, and organic matter in the NR-PM1. The statistics of this dataset are included in Table 7. Note that chloride is not included due to its concentration typically being below the detection limit of the instrument; a row for minimum value also is not included as the minimum for each species was below instrumental detection. The predominant species in terms of mass composition was organic matter (52.0%); this was followed by sulfate (32.9%), ammonium (11.6%), and nitrate (3.2%).

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Table 7. Statistics of the NR-PM1 dataset collected during the San Antonio field operations during summer 2017. Units are µg m-3. BDL indicates below detection limit of the HR-ToF-AMS.

Statistic Organics Sulfate Nitrate Ammonium NR-PM1

Average 5.42 3.43 0.33 1.21 10.43

Median 4.66 2.45 0.30 0.86 8.63

Maximum 81.07 32.43 6.96 10.15 88.05

25th %ile 3.14 1.62 0.22 0.59 6.43

75th %ile 6.97 4.11 0.41 1.47 11.94

The time series of all species during the campaign period (Figure 31) indicate significant

variability on relatively short time scales. Two high-loading periods also were observed during our campaign: May 9th – May 12th (at Traveler’s World) and May 27th–May 29th (at UTSA). High sulfate, ammonium, and organics signals are observed in both high periods.

Figure 31. Time series during the San Antonio field deployment of concentrations of HR-ToF-AMS-

measured NR-PM1 constituents, with two ‘high loading’ periods highlighted.

The time series of mass percentage of each species in the campaign period (Figure 4.2),

indicates negligible total nitrate and chloride signals and confirms that organics, sulfate, and ammonium species are the major components of NR-PM1.

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Figure 32. Time series during the San Antonio field deployment of relative composition of

HR-ToF-AMS-measured NR-PM1.

Focusing in on the time series of sulfate (Figure 33), four high sulfate events are

observed, with the last one (occurring May 27th) having the largest concentration. The diurnal profile of sulfate (Figure 34) indicates a relatively flat diurnal profile but with maximum average concentrations occurring in the middle of the day. The large 95th percentile whiskers indicate that sulfate concentrations are very variable.

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Figure 33. Time series of sulfate over the San Antonio field deployment.

Figure 34. Diurnal profile of sulfate aerosol: red line – average, bottom whisker – 5th percentile, box

bottom – 25th percentile, dash – median, box top – 75th percentile, top whisker – 95th percentile.

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Multiple high organics periods are also observed when considering the time series of organic matter (Figure 35); note the increase of the y-axis scale in Figure 35 relative to Figure 36. Some are not synchronous with high sulfate periods. From the diurnal profile (Figure 36), a slight bimodal profile is observed, with a minimum in late morning local time. The biomass burning events in nearby regions and Mexico could be a source that affects the organics level in the San Antonio area, but further source apportionment is needed to reach a reliable conclusion.

Figure 35. Time series of organic aerosol over the San Antonio field deployment.

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Figure 36. Diurnal profile of organic aerosol.

3.4. Planetary Boundary Layer (PBL) One of the new measurement capabilities added to the MAQL for this deployment was

the inclusion of a Vaisala CL-31 ceilometer. The CL-31 was installed on wire-rope isolators to reduce shock and vibrations to the instrument, which uses an eye-safe IR laser and detector (LIDAR) to measure and range backscatter from clouds and aerosols. Vaisala BL-View software processes the backscatter signals and determines up to three layers based on gradients in the data. Figure 37 shows an overall time series of data from this deployment for the first three layers. When using this software, visual inspection of the data is required to assess the physical meaning of the data. For instance, during the day the first layer may be expected to represent the PBL height, however should a local smoke plume that is not well mixed within the PBL pass over the site, the smoke plume would be identified as the first layer and the second layer would then represent the PBL. Once the smoke plume passed, the PBL would again be reported as the first layer. Similarly, on some evenings it is possible to detect the development of the nocturnal boundary layer which would be identified as layer 1. It is likely then that layer 2 would represent the PBL from earlier in the day, which may be considered the top of the residual layer. Figure 37 also shows cloud bases as detected by the CL-31. These data are separated from the three layers since they are not based on aerosol backscatter gradients as on a clear day, but they can approximate the PBL height on cloudy or overcast days. In all, 81% of the time the CL-31 was able to detect one or more aerosol layers, and clouds were detected in approximately 10% of the measurements.

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Further analysis of this data will be included in the upcoming project in conjunction with data from other sources, including radio and ozonesonde launches, radar and acoustic wind profilers, and the CL-31 at the UTSA site. Curiously, there are three periods where there does not seem to be much variation in the level 1 PBL heights, so these times will receive additional attention.

Figure 37. Overview of ceilometer measurements from the MAQL at Traveler’s World

3.5. VOC Canister Samples While Baylor’s PTR-MS can measure a wide variety of VOCs, there are many species

that it is unable to measure. Some of these unmeasured species are important to O3 production. Twenty-nine whole-air samples were collected during the campaign to extend the measurements from the PTR-MS. Section 3.7.2 below details the method used and results for extending the PTR-MS measurements for use in the LaRC model. With the approval of the TCEQ, Desert

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Research Institute’s (DRI) Organic Analytical Laboratory (OAL) (Reno, NV) was selected to provide and analyze the samples.

Each day during the campaign the TCEQ organized a conference call to discuss activities and forecasts. As part of this call, there was a discussion of the time and location for samples to be collected. Most often, samples were collected on days with coinciding ozonesonde launches. Mornings were typically sampled at the MAQL site at Traveler’s World shortly after the release of the pre-dawn ozonesonde launch. Afternoons were also coordinated with ozonesonde launches which were often conducted at the UTSA site. A selected group of canisters were sent with Aerodyne’s team to sites upwind of San Antonio for sampling.

Initial plans were to have the OAL provide a sampling system for the canisters, however upon receipt it was discovered the supplied system would pressurize the samples above ambient. In a humid environment such as San Antonio would cause condensation inside the cans, potentially leading to losses of analytes from the sample. With the assistance of the TCEQ, several glass orifices were loaned to the field team for sampling until a Passive Air Sampling Kit from Restek (Bellefonte, PA) was received. The Restek Sampling kit was tuned to fill the sample canisters in nominally 1 hour and leave the sample with about 2” Hg vacuum. Both the Restek sampler and glass orifices were purged with ultra-high purity N2 after each sample to purge ambient air from the system and prevent contamination of the next sample Figure 38.

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Figure 38. Purging the VOC sample inlet with N2 after sampling a canister. The vacuum pump draws N2 through the inlet at the same nominal flow rate as a canister. The tee on the inlet allows excess N2 to be vented to atmosphere.

In general, the samples were successful, however several of the canisters arrived with

open valves, and varying degrees of apparent leakage. These canisters were all flagged and arrangements were made with the OAL to exchange them for new cans. It is suspected that the valves may have vibrated open during transit. Unfortunately, upon OAL’s receipt of the sampled canisters several were found to be open as well, even though valve tightness was verified before shipping from Houston. One of the samples showed signs of contamination after analysis and were excluded from subsequent modeling and interpretation of the data. These results can be found in the data archive. Instructions for accessing the archive can be found in the Appendix A.

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3.6. Additional Measurements With the increased interest on the impacts of upwind oil and gas activities there was a

desire to measure methane at the Traveler’s World site. The project team received permission to add a methane measurement if an instrument could be located. Through a collaboration with Aerodyne, we were able to provide a small trailer that could be collocated with the MAQL to house an Aerodyne quantum cascade laser instrument. This instrument was tuned to measure both methane and ethane. UH provided the daily labor for the instrument while Aerodyne operated the system remotely via remote desktop software as well as managing the data processing and subsequent QA/QC.

In support of the San Antonio ozonesonde project (PGA 582-17-71351-11) an additional measurement site was established at the University of Texas – San Antonio campus in a parking lot on the southwest side of campus. The sampling trailer, provided by Baylor, was instrumented with O3, NO, NOX, NOy, CO, SO2, temperature, relative humidity, pressure, wind speed and direction, actinic flux (305-700 nm), and jNO2. A Pandora (Herman et al., 2015) instrument was also operated on the roof of the trailer to provide column measurements of O3 for comparison to the ozonesondes that were launched at this site. Data from the UTSA site will be analyzed in more detail during a future project.

3.7. Zero-Dimensional Box Modeling 3.7.1. Langley 0-D constrained steady state model Details on the NASA Langley Research Center (LaRC) time dependent box model can be

found in Olson et al. [2006] and Crawford et al. [1999]. In general, chemical reactions and kinetics are those recommended by Sander et al. [2006]. Non-methane hydrocarbon chemistry is based on the modified condensed scheme in Lurmann et al. [1986]. For this study, the model was run in a time-dependent mode using an assumption of diurnal steady state. The time-dependent mode is useful when observations of moderately-lived HOx precursor species are missing that cannot be adequately represented by photostationary steady state. A comparison of several model mechanisms, including the LaRC mechanism used here, can be found in Chen et al. [2010]. Overall the LaRC mechanism compared quite well to CB05, RACM, SAPRC-99, SAPRC-07, and MCMv3.1 for HO2, ˙OH, and O3 production in Houston. In addition to the standard model chemical constraints of measurements of O3, CO, and either NO or NO2, the model was also constrained by measurements of HCHO. Previous work shows that constraining to measurements of H2O2 and HNO3 have little impact on the calculated ozone production rates [Flynn et al., 2010]. Although the model can be constrained by other inputs, those data, such as HONO, were not available for this study. Input data was averaged or interpolated to 10-minute intervals and records were removed where one or more of the constraints mentioned above were missing. Data was also limited to periods with valid jNO2 measurements and solar zenith angles ≤ 90º. With this approach, approximately 99 hours of daytime data was modeled. Like other zero-dimensional models, the LaRC model does not include advection in the model, but it is considered during the analysis of the results. Model outputs include the calculated reaction rates, gas concentrations, and O3 formation and destruction rate terms.

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3.7.2. VOC lumping Nine ambient whole air canisters were collected at Traveler’s World. Table 8 shows the

samples IDs, date and time that were used for VOC correlations and ratio calculations, as described subsequently.

Table 8. List of nine canister samples collected at Traveler’s World to determine VOC correlations and ratios for use in modeling described subsequently.

pmi Date Time

P161M001I020 17/5/06 7:56 P161M001I018 17/5/12 8:02 P161M001I022 17/5/14 6:55 P161M001I010 17/5/24 17:24 P161M001I011 17/5/24 7:00 P161M001I034 17/5/24 7:00 P161M001I029 17/5/25 8:01 P161M001I009 17/5/27 8:02 P161M001I012 17/5/27 8:02

Four VOCs species were measured by both PTR-MS and canisters: isoprene, benzene,

toluene, and styrene. Through analysis of data for all VOC species in the canisters, good linear correlations between benzene and most of the VOCs captured by the canister samples were observed. This enables the calculation of average ratios of each species to benzene (Table 9). For certain VOC species whose concentration was below the minimum detection limit (MDL, 0.01 ppb) in some samples, we use half of the MDL (0.005 ppb) as their concentrations for ratio calculation to avoid extreme values. Once the ratios were determined, they were applied to the time series of benzene data measured by PTR-MS to estimate VOC concentrations time series.

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Table 9. Average ratio to and correlation coefficient (r2) with benzene for VOCs collected in nine canister samples at Traveler’s World.

VOC Species Average Ratio (VOC to Benzene) Correlation with Benzene

propene 3.35 0.96 c-2-butene 0.07 0.94 isobutylene 1.27 0.99 isopentane 6.87 0.93

cyclopentane 0.13 0.94 2-methylpentane 0.99 0.97 3-methylpentane 0.69 0.97

n-hexane 1.03 0.96 methylcyclopentane 0.23 0.94

n-heptane 0.14 0.93 methylcyclohexane 0.12 0.93

2-methylheptane 0.08 0.94 ethylbenzene 0.43 0.99

o-xylene 0.49 0.99 n-propylbenzene 0.14 0.93

A few species that were not correlated well with any of the four indicator species (r2 <

0.8) were not included in the modeling input file. Those are 1-pentene (max r2 = 0.76 with isoprene), 1,3-butadiene (max r2 = 0.39 with styrene), 1-butene (max r2 = 0.33 with benzene), 1-heptene (max r2 = 0.39 with styrene), and 2,2,4-trimethylpentane (max r2 = 0.39 with styrene).

The corresponding groups of measured VOC concentrations (from PTR-MS) and calculated VOC concentrations (from canister ratios) were lumped into alkanes, alkenes and aromatics based on their chemical properties. For VOC species that are measured in both PTR-MS and canisters, we use PTR-MS data because they reflect a direct measurement. In addition to low-correlation species omitted above, α-pinene, m-xylene, p-xylene and o-xylene are also omitted during the lumping process to avoid double counting in monoterpenes and xylenes. Table 10 lists the three major lumping categories, with italic text indicating those from the PTR-MS. Methane, ethane, propane, ethylene, isoprene, benzene, acetone, acetaldehyde, formaldehyde and methyl ethyl ketone (MEK) are not lumped into the three groups because they are pulled out explicitly as stand-alone species in the model. Remaining species are lumped on their chemical structure and hydroxyl radical reactivity as follows: Hydroxyacetone: lump with MEK; Methyl vinyl ketone and methacrolein (MVK+MACR): lump ½ into alkenes, ¼ into acetaldehyde and ¼ into MEK. The time series for these lumped species are shown in Figure 39 and Figure 40. Formaldehyde measurements from the PTR-MS in the MAQL were noisier than other VOCs. In order to reduce the point-to-point variability and subsequent changes in modeled

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O3 rates and VOC reactivity, the HCHO data were averaged to one-hour values and then interpolated to match the 10-minute time steps of the model, effectively smoothing the data but not changing the magnitude.

Table 10. Species lumping into generic groups for application in modeling described subsequently.

Alkanes Alkenes Aromatics

n-butane, isobutene, propene ethylbenzene 2,2-dimethylbutane, 2,3-dimethylbutane, c-2-butene xylenes

2-methylpentane, 3-methylpentane, isobutylene ethylbenzene n-pentane, n-heptane, n-hexane, t-2-butene isopropylbenzene

methylcyclopentane monoterpenes n-propylbenzene 2,4-dimethylpentane, 3-ethyltoluene

cyclohexane, cyclopentane, 4-ethyltoluene isopentane, n-octane o-ethyltoluene

2-methylheptane 1,3,5-trimethylbenzene 3-methylheptane 1,2,3-trimethylbenzene

methylcyclohexane c3-benzenes, c4-benzenes 2-methylheptane styrene, toluene

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Figure 39. Mixing ratio time series of lumped alkanes and alkenes (top), lumped aromatics and measured benzene (middle), and measured acetone and aldehydes (bottom).

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Figure 40. Mixing ratio time series of measured isoprene and MEK (top), ethane and ethene (middle), and propane and formaldehyde (bottom).

3.7.3. O3 production rates Using measured meteorology (including photolysis frequencies), measured nitrogen

oxides (NOX = nitric oxide (NO) + nitrogen dioxide (NO2)) and measured/estimated VOCs, the Langley Research Center (LaRC) model allows estimation of O3 formation rate (FO3), O3 destruction rate (DO3), and ˙OH reactivity of VOCs. Net O3 production rate (PO3) is the difference between FO3 and DO3 (PO3 = FO3 - DO3). The LaRC model output includes time series of modeled concentrations and reaction rates. Using this data diurnal profiles (during daytime hours) of these parameters can be calculated.

Based on the diurnal profile of FO3 (Figure 41), we see local O3 formation maximize around noon. In general, organic peroxy radical (RO2) and hydroperoxy radical (HO2) contribute approximately equally to FO3. On low PO3 days, O3 formation only appears to matter at rush hours in the afternoon. The suspected reason is that meteorological conditions are not favorable for O3 production until late afternoon (lower temperature, cloudy or rainy). On high PO3 days, diurnal profile is markedly different. The highest FO3 still appears at noontime, but it sustains at a very high level until the late afternoon. This could be attributed to the integrative effect of sunny weather, hot temperature and the abundance of O3 precursors in the ambient environment.

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Figure 41. FO3 diurnal profiles during the San Antonio campaign as estimated using the LaRC model. Profiles include the campaign average and those segregated by low and high PO3 days.

Based on the diurnal profile of DO3 (Figure 42), it is seen that O(1D) + H2O reaction and

NO2 + ˙OH reaction are the dominant O3 destruction pathways. On high PO3 days, HO2 + O3 reaction also contributes. On low PO3 days, NO2 + ˙OH dominates at rush hours. In general, DO3 is smaller than FO3 by approximately an order of magnitude. This leads to a large PO3.

Figure 42. DO3 diurnal profiles during the San Antonio campaign as estimated using the LaRC model.

Profiles include the campaign average and those segregated by low and high PO3 days.

3.7.4. VOC reactivity

Based on the diurnal profile of ˙OH reactivity of VOCs (Figure 43), it is observed that ˙OH reactivity is dominated by isoprene, followed by alkenes, formaldehyde, and aromatics. This is reasonable considering the abundant oak trees in the San Antonio area as well as the neighborhood of the sampling sites. Besides the ample emission sources and favorable hot weather, isoprene is highly reactive, making it the biggest contributor to total ˙OH reactivity no matter whether a low or high PO3 day is considered. Meanwhile, even though the absolute concentration of alkenes and aromatics are much smaller than alkanes (Figure 39 and Figure 40), their chemical structure enables them to have a higher reactivity. Very little ethane (C2H6) contribution to total ˙OH reactivity is observed.

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Figure 43. Hydroxyl radical reactivity diurnal profiles during the San Antonio campaign as estimated using the LaRC model. Profiles include the campaign average and those segregated by low and high PO3 days.

Plotting PO3 versus ˙OH reactivity of VOCs colored by the photolysis rate of NO2 (Figure 44) (jNO2), it is observed that the highest PO3 occurs when both ˙OH reactivity and jNO2 exhibit large values. However, unless the photolysis of certain times is very strong (jNO2 > 8*10-3 s-1), PO3 is relatively insensitive to the increase of ˙OH reactivity of VOCs. This once again supports our previous speculation that PO3 at the San Antonio area is NOX-sensitive.

Figure 44. PO3 versus ˙OH reactivity scaled by photolysis rate of NO2.

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3.7.5. NOX – VOC sensitivity

The relative importance of NOX and VOCs in O3 production changes based on their absolute concentrations and their concentrations relative to each other. Even though the relationship between O3, NOX and VOCs is driven by complex nonlinear photochemistry, it is commonly accepted to categorize O3 production into NOX-sensitive and VOC-sensitive (NOX-saturated) regimes. In the NOX-sensitive regime, mixing ratios typically are relatively low for NOX and relatively high for VOCs (high VOC/ NOX ratio). In this regime, PO3 increases with increasing NOX and is relatively insensitive in response to increasing VOCs. In the VOC-sensitive regime, the opposite is generally true. In the San Antonio area, VOCs are generally readily available, and the amount of O3 locally formed is determined by the amount of available NOX. From the plot of PO3 versus NO colored by times of the day (Figure 45), we observe a PO3 turnover point at a NO level of approximately ~1.5 to 2 ppbv. Since each dot in Figure 45 represents a 10-min time interval, the majority of samples fall to the left of the turnover point. Analysis of the NO data for the modeled periods reveal that ~80% of the NO values are 1.5 ppbv or less. This implies that during most of the modeled days, PO3 is NOX-sensitive. Unlike previous findings from Houston, there does not seem to be a clear dependence on time of day for the transition between NOX-sensitive and NOX-saturated regimes.

Figure 45. PO3 versus NO as predicted by the LaRC model.

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3.8. Data Archival UH currently maintains a data server (https://hoth.geosc.uh.edu) in the Earth &

Atmospheric Sciences server room on the UH campus with both FTP and web access. This server hosts much of the data shared between UH, Rice, and Baylor Universities, as well as with colleagues from other universities and organizations. File format varies as appropriate for each measurement, however when possible time-stamped delimited text files are utilized. Each group has a username and password, which allows them to access the appropriate folders with read-write privileges. Download access for individual files is often accomplished with a direct link, which can be limited to a certain time window and number of downloads. Internally the data is shared across multiple drives in order to provide redundancy in the event of the failure of one or more hard drives. Should physical damage occur to the server, an off-site backup has been established and is maintained at Rice University.

3.9. Presentation of results at national meetings The results for the LaRC photochemical box model were presented at the American

Geophysical Union’s Fall Meeting in New Orleans, LA (December 11–15, 2017). This work included the discussion of P(O3) in Section 3.7 above reporting the differences in O3 formation and destruction terms and speciated VOC reactivity from the MAQL measurements at Traveler’s World. This poster was shared with Jim Price and Mark Estes on December 1, 2017. This work will also be presented at the 20th Conference on Atmospheric Chemistry at the 98th Annual Meeting of the American Meteorological Society in Austin, TX (January 7–11, 2018).

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4. Conclusion and Recommendations 4.1. Conclusions The collaboration between UH, Rice, and Baylor to collect measurements in San Antonio to

investigate ozone (O3) and air quality was very successful. The first full day of measurements began with the Mobile Air Quality Lab (MAQL) at Traveler’s World on May 2nd, eight days ahead of the initial schedule. Measurements at the University of Texas - San Antonio (UTSA) site began on May 6 in the Baylor trailer. On May 27th, the MAQL relocated to the UTSA site for an intercomparison and calibration standard exchange with the UTSA trailer and Aerodyne measurements, and concluded on May 31st with the end of the campaign.

A sample conditioning system for the Proton Transfer Reaction – Mass Spectrometry (PTR-MS) was successfully constructed, improving the performance of the instrument and allowing for the quantification of formaldehyde (HCHO), although additional QA/QC is needed before final lower limit of detection (LLOD) and uncertainty values can be calculated.

The LaRC 0-dimensional box model, a constrained steady state model, was used to calculate O3 formation and destruction rates, contributions of specific and lumped Volatile organic compounds (VOCs) to the overall VOC+˙OH (volatile organic compound + hydroxyl radical) reactivity, and an assessment of the NOX-VOC sensitivity of this period. In total, 99 hours of daytime data were able to be modeled based on available data. Median diurnal profiles were calculated for all days as well as low and high P(O3) days. Hydroperoxy (HO2) and organic peroxy (RO2) free radicals appear to contribute equally to O3 formation in San Antonio. The largest single contributor to VOC reactivity was shown to be isoprene, followed by alkenes. Ethane concentrations did not contribute significantly to the overall reactivity. Analyses of the net O3 production rates vs NO concentrations find that the transition from NOX-sensitive to NOX-saturated (VOC-sensitive) regimes occur around 1.5-2 ppbv of NO based on the evaluation of Figure 45. Further evaluation of NO values revealed that ~80% of the modeled values were 1.5 ppbv or less, indicating that San Antonio is predominantly NOX sensitive during this campaign. Based on discussions with Ezra Wood, this is in agreement with his HOxROx measurements and subsequent O3 formation rate calculations, however the overall NOX levels upwind of San Antonio region, where most of the HOxROx measurements were collected by the Drexel and Aerodyne groups, were much lower than at Traveler’s World.

Specific VOC can serve as anthropogenic (AVOC), biogenic (BVOC), or biomass burning (BBVOC) tracers. Examples of AVOCs, BVOCs, and BBVOCs including toluene and benzene, isoprene and monoterpenes, and acetyldehyde and acetonitrile, respectively. At TW, the rush hour slopes for toluene:benzene and the average ratios are both in the range for traffic and other fossil sources. Within San Antonio, there are several urban vegetation types which are not necessarily native which were closest to the Traveler’s World site.

To trace the potential impact of biogenic VOC sources (BVOC) on the urban VOC in San Antonio, two sets of BVOCs were measured. Isoprene and monoterpene emissions vary by plant species, the monoterpene source was consistent across San Antonio, while the isoprene source had significant spatio-temporal differences. Within the San Antonio Field Study (SAFS) campaign, isoprene had very strong diurnal trends as expected, while monoterpenes had much lower mixing ratios at both sites and no diel trend. Roughly, the BVOC mixing ratios for both isoprene and monoterpenes was lower than measured in Houston in 2016 using the same PTRMS

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in the MAQL. There was a prominent BBVOC event in the first week of the campaign with elevated concentrations of both acetonitrile and acetaldehyde which was concurrent to agricultural burning events in Mexico taking place in and around the Yucatan Peninsula and transported across the Gulf of Mexico. Strong correlations were observed of these two tracers during this biomass burning event. There is no correlation between acetonitrile and acetaldehyde when data from the non-biomass burning period at Traveler’s World.

Although this project was initially planned with a focus on O3, aerosol measurements provided interesting results. Two high-loading periods also were observed during our campaign: May 9th May–12th (at Traveler’s World) and May 27th–May 29th (at UTSA). High sulfate, ammonium, and organics signals are observed in both high periods. Negligible total nitrate (3.2%) and chloride (typically below detection limit) signals confirm that organics (52%), sulfate (32.9%), and ammonium species (11.6%) are the major components of non-refractory submicron aerosols (NR-PM1 ) in San Antonio during this campaign. Biomass burning events in nearby regions and Mexico could be a source that affects the organics level in the San Antonio area, but further source apportionment and analysis of archived air quality forecasts from the TCEQ is needed to reach a reliable conclusion. Four high sulfate events were observed, with the last one (occurring May 27th) having the largest concentration. The diurnal profile of sulfate (Figure 34) indicates a relatively flat diurnal profile but with maximum average concentrations occurring in the middle of the day.

4.2. Recommendations and future work Going forward, additional testing and application of lessons learned from this campaign will

be applied to the PTR-MS sample conditioning system, with special attention paid to reducing uncertainty and LLOD for HCHO measurements.

It is anticipated that a project to provide a deeper analysis of the SAFS 2017 field data should begin in early 2018 and will include PIs from UH, Rice, Baylor, and St. Edwards University, as well as coordination with similar efforts likely to be undertaken by Aerodyne Research, Inc.. Areas to be examined will include the following:

• Perform a general site-to-site comparison to examine the spatial and temporal variability between sites to better understand conditions in the upwind, central, and downwind areas of San Antonio.

• Provide a more in-depth investigation of source contributions using data from both sites based on time of day, wind direction, particle tracers, and VOC composition.

• Conduct source apportionment of VOCs using Positive Matrix Factorization (PMF). PMF factor weightings will then be used to attribute a given fraction of VOCs to a specific source (Rutter, et al., 2015).

• Analyze filter-based PM collected by Baylor during SAFS to further characterize air quality events at UTSA. Analysis will pay special attention to high biomass burning and sulfate events and possible impacts from cement kilns.

• Investigate differences in O3 production efficiencies and VOC reactivity between the two sites using the Langley Research Center zero-dimensional model.

• Compare Rice and Aerodyne HR-ToF-AMS data during the instrument co-location at the UTSA site at the end of the campaign.

• Determine the source of very large sulfate concentrations observed in the San Antonio

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data. Additionally, seek to determine if organosulfates (OS) are detected in the HR-ToF-AMS mass spectra (Farmer et al., 2010).

• Identify long-range transport of biomass burning or upwind sources of pollution on O3 profiles using the NASA remote sensing data, filter and PTR-MS in situ data, backward trajectory analyses, O3 profile measurements, and surface trace gas observations.

• Examine sulfur transport into the San Antonio area and the potential mixing of Central American agricultural smoke plumes with industrial sources of sulfur, e.g. from oil and gas industry in southern Gulf of Mexico and sulfur from sea surfaces.

• Comparison of University of Texas acoustic sounding data with co-located weather balloon soundings at Calaveras Lake and UTSA during the May 2017 intensive period.

• Evaluation of orographic impacts on the vertical mixing of O3 and SO2 in San Antonio as demonstrated by University of Texas acoustic sounding systems at Calaveras Lake and UTSA, balloon sounding from Traveler’s World, UTSA, and Trinity University, ceilometer measurements from Traveler’s World and UTSA, and in situ surface trace gas data.

• Comparison of boundary layer heights as observed by weather balloon instrumentation, University of Texas acoustic soundings, and Vaisala ceilometer data from UTSA and Calaveras Lake.

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