The San Joaquin Valley (SJV) of California experiences high concentrations of particulate matter NHNO during episodes of meteorological stagnation in winter. A rich data set of observations related to NHNO formation was acquired during multiple periods of elevated NHNO during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) field campaign in SJV in January and February 2013. Here NHNO is simulated during the SJV DISCOVER-AQ study period with the Community Multiscale Air Quality (CMAQ) model, diagnostic model evaluation is performed using the DISCOVER-AQ data set, and integrated reaction rate analysis is used to quantify HNO production rates. Simulated NO generally agrees well with routine monitoring of 24-hr average NO, but comparisons with hourly average NO measurements in Fresno revealed differences at higher time resolution. Predictions of gas-particle partitioning of total nitrate (HNO + NO) and NHx (NH + NH) generally agree well with measurements in Fresno, although partitioning of total nitrate to HNO is sometimes overestimated at low relative humidity in afternoon. Gas-particle partitioning results indicate that NHNO formation is limited by HNO availability in both the model and ambient. NH mixing ratios are underestimated, particularly in areas with large agricultural activity, and additional work on the spatial allocation of NH emissions is warranted. During a period of elevated NHNO, the model predicted that the OH + NO pathway contributed 46% to total HNOproduction in SJV and the NO heterogeneous hydrolysis pathway contributed 54%. The relative importance of the OH + NO pathway for HNO production is predicted to increase as NOx emissions decrease.
Modeled source attribution information from the Community Multiscale Air Quality model was coupled with ambient data from the 2011 Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality Baltimore field study. We assess source contributions and evaluate the utility of using aircraft measured CO and NOy relationships to constrain emission inventories. We derive ambient and modeled ΔCO:ΔNOy ratios that have previously been interpreted to represent CO:NOy ratios in emissions from local sources. Modeled and measured ΔCO:ΔNOy are similar; however, measured ΔCO:ΔNOy has much more daily variability than modeled values. Sector‐based tagging shows that regional transport, on‐road gasoline vehicles, and nonroad equipment are the major contributors to modeled CO mixing ratios in the Baltimore area. In addition to those sources, on‐road diesel vehicles, soil emissions, and power plants also contribute substantially to modeled NOy in the area. The sector mix is important because emitted CO:NOx ratios vary by several orders of magnitude among the emission sources. The model‐predicted gasoline/diesel split remains constant across all measurement locations in this study. Comparison of ΔCO:ΔNOy to emitted CO:NOy is challenged by ambient and modeled evidence that free tropospheric entrainment, and atmospheric processing elevates ambient ΔCO:ΔNOy above emitted ratios. Specifically, modeled ΔCO:ΔNOy from tagged mobile source emissions is enhanced 5–50% above the emitted ratios at times and locations of aircraft measurements. We also find a correlation between ambient formaldehyde concentrations and measured ΔCO:ΔNOy suggesting that secondary CO formation plays a role in these elevated ratios. This analysis suggests that ambient urban daytime ΔCO:ΔNOy values are not reflective of emitted ratios from individual sources.
Abstract. Analysis of formaldehyde measurements by the Pandora spectrometer systems between 2016 and 2019 suggested that there was a temperature-dependent process inside the Pandora head sensor that emitted formaldehyde. Some parts in the head sensor were manufactured from the thermal plastic polyoxymethylene homopolymer (E.I. Du Pont de Nemour & Co., USA; POM-H Delrin®) and were responsible for formaldehyde production. Laboratory analysis of the four Pandora head sensors showed that internal formaldehyde production had exponential temperature dependence with a damping coefficient of 0.0911±0.0024 ∘C−1 and the exponential function amplitude ranging from 0.0041 to 0.049 DU. No apparent dependency on the head sensor age and heating and cooling rates was detected. The total amount of formaldehyde internally generated by the POM-H Delrin components and contributing to the direct-sun measurements were estimated based on the head sensor temperature and solar zenith angle of the measurements. Measurements in winter, during colder (< 10 ∘C) days in general, and at high solar zenith angles (> 75∘) were minimally impacted. Measurements during hot days (> 28 ∘C) and small solar zenith angles had up to 1 DU (2.69×1016 molec. cm−2) contribution from POM-H Delrin parts. Multi-axis differential slant column densities were minimally impacted (<0.01 DU) due to the reference spectrum being collected within a short time period with a small difference in head sensor temperature. Three new POM-H Delrin free Pandora head sensors (manufactured in summer 2019) were evaluated for temperature-dependent attenuation across the entire spectral range (300 to 530 nm). No formaldehyde absorption or any other absorption above the instrumental noise was observed across the entire spectral range.
Formaldehyde column density (ΩHCHO) showed a potentially useful correlation with surface ozone during the LISTOS campaign on Long Island Sound and the KORUS‐AQ campaign in Seoul, South Korea. This builds on previous work that identified this relationship from in situ aircraft observations with similar findings for ground‐based and airborne remote sensing of ΩHCHO. In the Long Island Sound region, ΩHCHO and surface ozone exhibited strong temporal (r2 = 0.66) and spatial (r2 = 0.73) correlation. The temporal variability in ΩHCHO (∼1 Dobson units [DU]) was larger than the range in the spatial average (∼0.1 DU). The spatial average is most useful for informing ozone monitoring strategies, demonstrating the challenge in using ΩHCHO satellite data sets for this purpose. In Seoul, high levels of NO2 resulted in Ox better correlating with ΩHCHO than surface ozone due to titration effects. The ΩHCHO–Ox relationship may therefore reflect the sum of surface ozone and related photochemical oxidants, relevant to air quality standards set to regulate this quantity such as the U.S. EPA National Ambient Air Quality Standard (NAAQS). The relationship of ΩHCHO to Ox shifted in Seoul during the campaign demonstrating the need to evaluate this relationship over longer time periods. With sufficient precision in future satellite retrievals, ΩHCHO observations could be useful for evaluating the adequacy of surface air quality monitoring strategies.
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