Dynamic and chemical controls on new particle formation occurrence and characteristics from in situ and satellite-based measurements

Sullivan, Ryan C.; Pryor, Sara C.

doi:10.1016/j.atmosenv.2015.12.050

Cited by 4 publications

(11 citation statements)

References 47 publications

(71 reference statements)

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“…To quantify similarities and differences in NPF frequency, persistence, and seasonality across North America, an automated methodology is applied to each of the PSD data sets to identify event occurrence and estimate J n , GR, and SP (an earlier version of the approach was described in Sullivan and Pryor []). In brief, a NPF event is reported, and included in the analysis, if: The minimum nucleation mode geometric mean diameter (<30 nm; D g Nuc ) occurs within 10 h of the peak nucleation mode number concentration and while the difference in the geometric mean diameter for D p < 100 nm ( D g ) and D g Nuc is less than or equal to 10 nm. The r 2 of the regression fit for the GR calculation (tracking D g Nuc from event start to +3 h) is ≥0.5. And the event metrics can be reasonably calculated (e.g., GR and J n > 0; equations ):

J_{n} = \frac{d N_{normaln normalu normalc}}{d t} + F_{coag} + F_{growth} \dots

where F_{coag} = true {true\sum}_{i = min (D_{p})}^{i = 30 nm} ([], \frac{1}{2} K_{i i} N_{i} + true {true\sum}_{j = i + Δ D_{p}}^{j = max (D_{p})} ((), K_{i j} N_{j})) \dots

and F_{growth} = true {true\sum}_{i = min (D_{p})}^{i = 30 nm} ([], \frac{N_{i}}{30 normalnm - D_{pi}} * normalG normalR) \dots

normalS normalP =

…”

Section: Methodsmentioning

confidence: 99%

Using satellite‐based measurements to explore spatiotemporal scales and variability of drivers of new particle formation

et al. 2016

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New particle formation (NPF) can potentially alter regional climate by increasing aerosol particle (hereafter particle) number concentrations and ultimately cloud condensation nuclei. The large scales on which NPF is manifest indicate potential to use satellite‐based (inherently spatially averaged) measurements of atmospheric conditions to diagnose the occurrence of NPF and NPF characteristics. We demonstrate the potential for using satellite‐based measurements of insolation (UV), trace gas concentrations (sulfur dioxide (SO2), nitrogen dioxide (NO2), ammonia (NH3), formaldehyde (HCHO), and ozone (O3)), aerosol optical properties (aerosol optical depth (AOD) and Ångström exponent (AE)), and a proxy of biogenic volatile organic compound emissions (leaf area index (LAI) and temperature (T)) as predictors for NPF characteristics: formation rates, growth rates, survival probabilities, and ultrafine particle (UFP) concentrations at five locations across North America. NPF at all sites is most frequent in spring, exhibits a one‐day autocorrelation, and is associated with low condensational sink (AOD × AE) and HCHO concentrations, and high UV. However, there are important site‐to‐site variations in NPF frequency and characteristics, and in which of the predictor variables (particularly gas concentrations) significantly contribute to the explanatory power of regression models built to predict those characteristics. This finding may provide a partial explanation for the reported spatial variability in skill of simple generalized nucleation schemes in reproducing observed NPF. In contrast to more simple proxies developed in prior studies (e.g., based on AOD, AE, SO2, and UV), use of additional predictors (NO2, NH3, HCHO, LAI, T, and O3) increases the explained temporal variance of UFP concentrations at all sites.

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J_{n} = \frac{d N_{normaln normalu normalc}}{d t} + F_{coag} + F_{growth} \dots

where F_{coag} = true {true\sum}_{i = min (D_{p})}^{i = 30 nm} ([], \frac{1}{2} K_{i i} N_{i} + true {true\sum}_{j = i + Δ D_{p}}^{j = max (D_{p})} ((), K_{i j} N_{j})) \dots

and F_{growth} = true {true\sum}_{i = min (D_{p})}^{i = 30 nm} ([], \frac{N_{i}}{30 normalnm - D_{pi}} * normalG normalR) \dots

normalS normalP =

…”

Section: Methodsmentioning

confidence: 99%

Using satellite‐based measurements to explore spatiotemporal scales and variability of drivers of new particle formation

et al. 2016

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“…At both MMSF and Egbert, the past values of UFP (at time t − 1 for Egbert and up to t − 2 for MMSF) appear to be the main determinants of current UFP concentrations, which highlights the strong temporal autocorrelation of UFP and supports the postulate of regional‐scale NPF events [ Crippa and Pryor , ; Sullivan and Pryor , ]. Besides the intercept, other significant predictors of UFP are UV and AOD, which are the predictors that are characterized by higher accuracy (Table ).…”

Section: Resultsmentioning

confidence: 59%

“…UFP measurements in eastern North America exhibit strong autocorrelation [ Crippa and Pryor , ; Jeong et al , ; Sullivan et al , ; Sullivan and Pryor , ]; thus, we propose a functional form that allows use of only concurrent satellite observations as predictors and also one in which past particle number concentrations are also included in the proxy algorithm to allow an investigation of the dependence of particle concentration from its past values. More specifically, the proxy algorithm is designed to estimate UFP at time t based on UFP up to time t − 1 and all five satellite‐retrieved properties at time t , separately for the two sites.…”

Section: Methodsmentioning

confidence: 99%

“…A proxy based on NO 2 /AOD exhibited an R 2 with observations at two sites of 0.24 and 0.09 ( n = 49 and 124), respectively. A key finding in this research was the substantial degradation in proxy skill when a direct measurement of the condensational sink at the site is replaced by the satellite‐derived AOD in the denominator. Sullivan and Pryor [] sought to expand the suite of predictors drawn from remote sensing observations to include a measure of biological activity and thus potential for release of biogenic volatile organic compounds (the cross‐product of leaf area index (LAI) and surface temperature), SO 2 , NO 2 , formaldehyde (HCHO) (as a proxy for production of semivolatile organic oxidation products), UV, the cross product of AE and AOD (as a proxy for the condensational sink), and the cross product of UV and SO 2 (as a proxy for sulfuric acid production). They also sought to quantify other descriptors of nucleation events: the occurrence (modeled by using logistic regression), formation rates, growth rates, and survival probabilities (modeled by using MLR) using data from the MMSF site.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting ultrafine particle concentrations from satellite and in situ observations

2017

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Recent innovations in remote sensing technologies and retrievals offer the potential for predicting ultrafine particle (UFP) concentrations from space. However, the use of satellite observations to provide predictions of near‐surface UFP concentrations is limited by the high frequency of incomplete predictor values (due to missing observations), the lack of models that account for the temporal dependence of UFP concentrations, and the large uncertainty in satellite retrievals. Herein we present a novel statistical approach designed to address the first two limitations. We estimate UFP concentrations by using lagged estimates of UFP and concurrent satellite‐based observations of aerosol optical properties, ultraviolet solar radiation flux, and trace gas concentrations, wherein an expectation maximization algorithm is used to impute missing values in the satellite observations. The resulting model of UFP (derived by using an autoregressive moving average model with exogenous inputs) explains 51 and 28% of the day‐to‐day variability in concentrations at two sites in eastern North America.

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“…Example particle size distribution (PSD) at 28 m from the FMPS operated on the gradient sampling system over a 15 day period during 14-28 September 2013 (see details of the gradient sampling system given in Pryor et al [2014]). Also shown (by the magenta blocks) are days that are identified as clear nucleation events by the automated protocol presented in Sullivan and Pryor [2016]. This event classification was used to identify class-A type nucleation events that are used herein to examine UFP number fluxes during new particle formation.…”

Section: 1002/2016jd025854mentioning

confidence: 99%

Ultrafine particle number fluxes over and in a deciduous forest

et al. 2017

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Ultrafine particles (UFP, particles with diameters (Dp) < 100 nm) play a key role in climate forcing; thus, there is interest in improved understanding of atmosphere‐surface exchange of these particles. Long‐term flux measurements from a deciduous forest in the Midwestern USA (taken during December 2012 to May 2014) show that although a substantial fraction of the data period indicates upward fluxes of UFP, on average, the forest is a net sink for UFP during both leaf‐active and leaf‐off periods. The overall mean above‐canopy UFP number flux computed from this large data set is −4.90 × 106 m−2 s−1 which re‐emphasizes the importance of these ecosystems to UFP removal from the atmosphere. Although there remain major challenges to accurate estimation of the UFP number flux and in drawing inferences regarding the actual surface exchange from measurements taken above the canopy, the above the canopy mean flux is shown to be downward throughout the day (except at 23.00) with largest‐magnitude fluxes during the middle of the day. On average, nearly three quarters of the total UFP capture by this ecosystem occurs at the canopy. This fraction increases to 78% during the leaf‐active period, but the over‐storey remains dominant over the subcanopy even during the leaf‐off period.

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Dynamic and chemical controls on new particle formation occurrence and characteristics from in situ and satellite-based measurements

Cited by 4 publications

References 47 publications

Using satellite‐based measurements to explore spatiotemporal scales and variability of drivers of new particle formation

Using satellite‐based measurements to explore spatiotemporal scales and variability of drivers of new particle formation

Forecasting ultrafine particle concentrations from satellite and in situ observations

Ultrafine particle number fluxes over and in a deciduous forest

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