Stationary Pittsburgh Supersite Data for Center for Air, Climate, and Energy Solutions (CACES)

This item contains aerosol data collected as part of CACES in Pittsburgh, PA at a stationary super site. The super site was located on the Carnegie Mellon University campus, which is an urban background location. Data collection methods include aerosol mass spectrometer (AMS), gas monitors, BC, and PM2.5 from reference-grade instruments.The AMS data files include PM species (organics, sulfate, nitrate, etc) as well as PMF-resolved organic aerosol factors for traffic, cooking, and other sources.</div

Gas-particle partitioning of primary organic aerosol emissions: (1) Gasoline vehicle exhaust

<p>The gas-particle partitioning of the primary organic aerosol (POA) emissions from fifty-one light-duty gasoline vehicles (model years 1987–2012) was investigated at the California Air Resources Board Haagen–Smit Laboratory. Each vehicle was operated over the cold-start unified cycle on a chassis dynamometer and its emissions were sampled using a constant volume sampler. Four independent yet complementary approaches were used to investigate POA gas-particle partitioning: sampling artifact correction of quartz filter data, dilution from the constant volume sampler into a portable environmental chamber, heating in a thermodenuder, and thermal desorption/gas chromatography/mass spectrometry analysis of quartz filter samples. This combination of techniques allowed gas-particle partitioning measurements to be made across a wide range of atmospherically relevant conditions – temperatures of 25–100 °C and organic aerosol concentrations of−3. The gas-particle partitioning of the POA emissions varied continuously over this entire range of conditions and essentially none of the POA should be considered non-volatile. Furthermore, for most vehicles, the low levels of dilution used in the constant volume sampler created particle mass concentrations that were greater than a factor of 10 or higher than typical ambient levels. This resulted in large and systematic partitioning biases in the POA emission factors compared to more dilute atmospheric conditions, as the POA emission rates may be over-estimated by nearly a factor of four due to gas-particle partitioning at higher particle mass concentrations. A volatility distribution was derived to quantitatively describe the measured gas-particle partitioning data using absorptive partitioning theory. Although the POA emission factors varied by more than two orders of magnitude across the test fleet, the vehicle-to-vehicle differences in gas-particle partitioning were modest. Therefore, a single volatility distribution can be used to quantitatively describe the gas-particle partitioning of the entire test fleet. This distribution is designed to be applied to quartz filter POA emission factors in order to update emissions inventories for use in chemical transport models.</p

Intracity Variability of Particulate Matter Exposure Is Driven by Carbonaceous Sources and Correlated with Land-Use Variables

Localized primary emissions of carbonaceous aerosol are the major drivers of intracity variability of submicron particulate matter (PM1) concentrations. We investigated spatial variations in PM1 composition with mobile sampling in Pittsburgh, Pennsylvania, United States and performed source-apportionment analysis to attribute primary organic aerosol (OA) to traffic (HOA) and cooking OA (COA). In high-source-impact locations, the PM1 concentration is, on average, 2 μg m–3 (40%) higher than urban background locations. Traffic emissions are the largest source contributing to population-weighted exposures to primary PM. Vehicle-miles traveled (VMT) can be used to reliably predict the concentration of HOA and localized black carbon (BC) in air pollutant spatial models. Restaurant count is a useful but imperfect predictor for COA concentration, likely due to highly variable emissions from individual restaurants. Near-road cooking emissions can be falsely attributed to traffic sources in the absence of PM source apportionment. In Pittsburgh, 28% and 9% of the total population are exposed to >1 μg m–3 of traffic- and cooking-related primary emissions, with some populations impacted by both sources. The source mix in many U.S. cities is similar; thus, we expect similar PM spatial patterns and increased exposure in high-source areas in other cities.</p

Air Quality in Puerto Rico in the Aftermath of Hurricane Maria: A Case Study on the Use of Lower-Cost Air Quality Monitors

In the aftermath of Hurricane Maria, the electricity grid in Puerto Rico was devastated, with over 90% of the island without electricity; as of December 2017, about 50% of the island lacked electricity, and power outages were common elsewhere. Backup generators are widely used, sometimes as the main source of electricity. The hurricane also damaged the island’s existing air monitoring network and the University of Puerto Rico’s observing facilities. We deployed four lower-cost air quality monitors (Real-time Affordable Multi-Pollutant or RAMP monitors) and a black carbon (BC) monitor in the San Juan Metro Area in November 2017. The first month of data collected with the RAMPs showed high sulfur dioxide (SO₂) and carbon monoxide (CO) concentrations of varying magnitudes each night. SO₂ and CO are strongly correlated (r² >0.9) at two sites ~5 km apart (University of Puerto Rico and an industrial area, Puerto Nuevo), suggesting a single source type. BC measured at the UPR site is also well correlated with CO and SO₂. While the RAMPs are not certified as a federal equivalent method, the RAMP SO₂ data suggest that the EPA’s daily 1-hour threshold for SO₂ (75 ppb) was exceeded on almost 80% of the first 30 days of deployment (November-December 2017). The widespread reliance on generators for regular electric supply in the aftermath of Hurricane Maria appears to have increased air pollution in San Juan