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Abstract. We present a novel photolytic source of gas-phase NO3 suitable for use in atmospheric chemistry studies that has several advantages over traditional sources that utilize NO2 + O3 reactions and/or thermal dissociation of dinitrogen pentoxide (N2O5). The method generates NO3 via irradiation of aerated aqueous solutions of ceric ammonium nitrate (CAN, (NH4)2Ce(NO3)6) and nitric acid (HNO3) or sodium nitrate (NaNO3). We present experimental and model characterization of the NO3 formation potential of irradiated CAN / HNO3 and CAN / NaNO3 mixtures containing [CAN] = 10−3 to 1.0 M, [HNO3] = 1.0 to 6.0 M, [NaNO3] = 1.0 to 4.8 M, photon fluxes (I) ranging from 6.9 × 1014 to 1.0 × 1016 photons cm−2 s−1, and irradiation wavelengths ranging from 254 to 421 nm. NO3 mixing ratios ranging from parts per billion to parts per million by volume were achieved using this method. At the CAN solubility limit, maximum [NO3] was achieved using [HNO3] ≈ 3.0 to 6.0 M and UVA radiation (λmax⁡ = 369 nm) in CAN / HNO3 mixtures or [NaNO3] ≥ 1.0 M and UVC radiation (λmax⁡ = 254 nm) in CAN / NaNO3 mixtures. Other reactive nitrogen (NO2, N2O4, N2O5, N2O6, HNO2, HNO3, HNO4) and reactive oxygen (HO2, H2O2) species obtained from the irradiation of ceric nitrate mixtures were measured using a NOx analyzer and an iodide-adduct high-resolution time-of-flight chemical ionization mass spectrometer (HR-ToF-CIMS). To assess the applicability of the method for studies of NO3-initiated oxidative aging processes, we generated and measured the chemical composition of oxygenated volatile organic compounds (OVOCs) and secondary organic aerosol (SOA) from the β-pinene + NO3 reaction using a Filter Inlet for Gases and AEROsols (FIGAERO) coupled to the HR-ToF-CIMS. 

Abstract Background West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement. Methods We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill. Results Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill. Conclusions Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases). Graphical Abstract 

Glass transitions from liquid to semi-solid and solid phase states have important implications for reactivity, growth, and cloud-forming (cloud condensation nuclei and ice nucleation) capabilities of secondary organic aerosols (SOAs). The small size and relatively low mass concentration of SOAs in the atmosphere make it difficult to measure atmospheric SOA glass transitions using conventional methods. To circumvent these difficulties, we have adapted a new technique for measuring glass-forming properties of atmospherically relevant organic aerosols. Aerosol particles to be studied are deposited in the form of a thin film onto an interdigitated electrode (IDE) using electrostatic precipitation. Dielectric spectroscopy provides dipole relaxation rates for organic aerosols as a function of temperature (373 to 233 K) that are used to calculate the glass transition temperatures for several cooling or heating rates. IDE-enabled broadband dielectric spectroscopy (BDS) was successfully used to measure the kinetically controlled glass transition temperatures of aerosols consisting of glycerol and four other compounds with selected cooling and heating rates. The glass transition results agree well with available literature data for these five compounds. The results indicate that the IDE-BDS method can provide accurate glass transition data for organic aerosols under atmospheric conditions. The BDS data obtained with the IDE-BDS technique can be used to characterize glass transitions for both simulated and ambient organic aerosols and to model their climate effects.