Comment on amt-2021-263

doi:10.5194/amt-2021-263-rc2

Abstract

Organic nitrate (RONO2) formation in the atmosphere represents a sink of NOx (NOx = NO + NO2) and termination of the NOx/HOx (HOx = HO2 + OH) ozone formation and radical propagation cycles, can act as a NOx reservoir transporting reactive nitrogen, and contributes to secondary organic aerosol formation. While some fraction of RONO2 is thought to reside in the particle phase, particle-phase organic nitrates (pRONO2) are infrequently measured and thus poorly understood. There is an increasing prevalence of aerosol mass spectrometer (AMS) instruments, which have shown promise for determining quantitative total organic nitrate functional group contribution to aerosols. A simple approach that relies on the relative intensities of NO+ and NO2+ ions in the AMS spectrum, the calibrated NOx+ ratio for NH4NO3, and the inferred ratio for pRONO2 has been proposed as a way to apportion the total nitrate signal to NH4NO3 and pRONO2. This method is increasingly being applied to field and laboratory data. However, the methods applied have been largely inconsistent and poorly characterized, and therefore, a detailed evaluation is timely. Here, we compile an extensive survey of NOx+ ratios measured for various pRONO2 compounds and mixtures from multiple AMS instruments, groups, and laboratory and field measurements. We show that, in the absence of pRONO2 standards, the pRONO2 NOx+ ratio can be estimated using a ratio referenced to the calibrated NH4NO3 ratio, a so-called Ratio-of-Ratios method (RoR = 2.75 ± 0.41). We systematically explore the basis for quantifying pRONO2 (and NH4NO3) with the RoR method using ground and aircraft field measurements conducted over a large range of conditions. The method is compared to another AMS method (positive matrix factorization, PMF) and other pRONO2 and related (e.g., total gas + particle RONO2) measurements, generally showing good agreement/correlation. A broad survey of ground and aircraft AMS measurements shows a pervasive trend of higher fractional contribution of pRONO2 to total nitrate with lower total nitrate concentrations, which generally corresponds to shifts from urban-influenced to rural/remote regions. Compared to ground campaigns, observations from all aircraft campaigns showed substantially lower pRONO2 contributions at mid ranges of total nitrate (0.01–0.1 up to 2–5 μg m−3), suggesting that the balance of effects controlling NH4NO3 and pRONO2 formation and lifetimes — such as higher humidity, lower temperatures, greater dilution, different sources, higher particle acidity, and pRONO2 hydrolysis (possibly accelerated by particle acidity) — favors lower pRONO2 contributions for those environments and altitudes sampled.

Highlights

170 sets‌‌where‌R‌ N‌ H4NO3‌ ‌was‌‌reported‌‌high”).‌‌Those‌‌authors‌‌state‌‌that‌‌their‌‌approach‌‌represents‌‌a‌‌lower‌‌limit‌‌of‌‌ pRONO2‌.‌ ‌‌Similarly,‌‌Brito‌‌et‌‌al.‌‌(2018),‌‌Schulz‌‌et‌‌al.‌‌(2018),‌‌Huang‌‌et‌‌al.‌‌(2019a,‌‌2019b),‌‌and‌‌Avery‌‌et‌‌al.‌‌ (2019),‌‌applied‌‌a‌‌fixed‌R‌ p‌RONO2‌ ‌of‌‌0.1‌‌(citing‌‌Kiendler-Sharr‌‌et‌‌al.‌‌(2016))‌‌for‌‌aircraft‌‌measurements‌‌in‌‌ West‌‌Africa,‌‌aircraft‌‌measurements‌‌in‌‌the‌‌Amazon,‌‌rural‌‌forest‌‌and‌‌urban‌‌sites‌‌in‌‌Germany,‌‌and‌‌seasonal‌‌ variations‌‌of‌‌indoor/outdoor‌‌air,‌‌respectively.‌‌The‌‌same‌‌method‌‌has‌‌been‌‌applied‌‌to‌‌laboratory‌‌studies‌‌of‌‌
They‌‌estimated‌‌lower‌‌(2.2)‌‌and‌‌upper‌‌(4.4)‌‌limits‌‌for‌R‌ oR‌‌(or‌R‌ p‌RONO2‌ ‌=‌‌0.1-0.2‌‌for‌‌their‌‌corresponding‌‌ RN‌ H4NO3)‌ ‌‌from‌‌literature‌‌reports‌‌of‌‌SOA‌‌formed‌‌from‌‌isoprene+NO3‌‌ ‌radicals‌‌(Bruns‌‌et‌‌al.,‌‌2010)‌‌and‌‌ β-pinene+NO3‌‌ ‌radicals‌‌(Fry‌‌et‌‌al.,‌‌2009;‌‌Bruns‌‌et‌‌al.,‌‌2010;‌‌Boyd‌‌et‌‌al.,‌‌2015),‌‌respectively.‌‌The‌‌rationale‌‌ 185 for‌‌their‌‌approach‌‌is‌‌that,‌‌for‌‌their‌‌region‌‌of‌‌study,‌‌those‌‌two‌‌BVOC‌‌may‌‌represent‌‌major‌‌contributions‌‌to‌‌ the‌‌mixture‌‌of‌‌pRONO2‌,‌ ‌‌and‌‌that‌‌the‌‌literature‌‌suggests‌‌there‌‌may‌‌be‌‌some‌‌source/composition‌‌ dependence‌‌of‌R‌ p‌RONO2.‌ ‌‌For‌‌the‌‌same‌‌region,‌‌Chen‌‌et‌‌al.‌‌(2020)‌‌used‌‌bounds‌‌of‌R‌ p‌RONO2‌(‌ 0‌ .1-0.2)‌,‌‌‌based‌‌on‌‌ similar‌‌logic,‌‌however‌‌not‌‌derived‌‌from‌‌a‌R‌ oR‌‌calculation‌‌(however‌‌equivalent‌‌to‌‌a‌R‌ oR‌‌of‌‌1.7-3.3).‌‌In‌‌a‌‌ study‌‌of‌‌pRONO2‌‌ ‌and‌‌SOA‌‌formation‌‌from‌‌Alberta‌‌oil‌‌sands‌‌extraction‌‌emissions‌‌from‌‌ground‌‌and‌‌ 190 aircraft‌‌measurements,‌‌Lee‌‌et‌‌al.‌‌(2019)‌‌used‌‌the‌‌same‌‌bounds‌‌of‌R‌ p‌RONO2‌(‌ 0‌ .1-0.2)‌,‌‌‌also‌‌not‌‌derived‌‌from‌‌ a‌R‌ oR‌‌calculation‌‌and‌‌citing‌‌Xu‌‌et‌‌al‌‌(2015a)‌‌and‌‌Farmer‌‌et‌‌al.‌‌(2010)‌‌(equivalent‌‌to‌‌a‌R‌ oR‌‌of‌‌1.4-2.9‌‌and‌‌ 1.5-3.0‌‌for‌‌the‌‌two‌‌datasets).‌‌The‌‌same‌‌methods‌‌as‌‌Xu‌‌et‌‌al.‌‌(2015a)‌‌were‌‌used‌‌(applying‌‌the‌‌same‌‌range‌‌ of‌R‌ oR)‌,‌‌for‌‌measurements‌‌conducted‌‌in‌‌Houston,‌‌TX‌‌(Dai‌‌et‌‌al.,‌‌2019)‌‌and‌‌the‌‌North‌‌China‌‌Plain‌‌(Xu‌‌et‌‌ al.,‌‌2021).‌‌However‌‌Xu‌‌et‌‌al.‌‌(2021)‌‌adjusted‌‌the‌R‌ N‌ H4NO3‌ ‌to‌‌match‌‌the‌‌highest‌‌NO2‌+‌ /‌ NO+‌‌ ‌ratios‌‌observed,‌‌ 195 since‌‌it‌‌was‌‌substantially‌‌higher‌‌than‌‌the‌‌calibration‌R‌ N‌ H4NO3‌ ‌(assuming‌‌for‌‌those‌‌periods,‌‌nitrate‌‌was‌‌ purely‌‌NH4‌N‌ O3‌)‌ .‌‌Thus,‌‌those‌‌five‌‌studies‌‌report‌‌their‌‌concentrations‌‌and‌‌inorganic/organic‌‌nitrate‌‌split‌‌ ,‌‌and‌‌report‌‌lower‌‌and‌‌upper‌‌bounds;‌‌however,‌‌Lee‌‌et‌‌al.,‌‌(2019)‌‌largely‌‌focused‌‌on‌‌results‌‌for‌‌ the‌‌upper‌‌limit‌‌pRONO2‌‌‌concentrations‌‌for‌‌the‌‌scientific‌‌analysis‌‌(with‌‌equivalent‌R‌ oRs:‌‌‌1.4/1.5).‌‌Zhou‌‌et‌‌ al.‌‌(2016),‌‌Zhu‌‌et‌‌al.‌‌(2016),‌‌and‌‌Yu‌‌et‌‌al.‌‌(2019)‌‌applied‌‌the‌R‌ oR‌‌concept,‌‌citing‌‌a‌‌range‌‌of‌‌2–4‌‌from‌‌the‌‌ 200 literature,‌‌and‌‌thus‌‌reported‌‌estimated‌‌lower/upper‌‌limit‌‌averages‌‌for‌‌contribution‌‌of‌‌pRONO2‌‌ ‌to‌‌pNO3‌‌ ‌in‌‌ New‌‌York‌‌City‌‌(summer,‌‌67%/95%),‌‌a‌‌background‌‌site‌‌in‌‌China‌‌(spring,‌‌15/22%),‌‌and‌‌an‌‌urban‌‌site‌‌in‌‌ China‌‌(during‌‌spring,‌‌13%/21%;‌‌summer,‌‌41%/64%;‌‌autumn,‌‌16%/25%),‌‌respectively.‌‌Similarly‌‌Zhu‌‌et‌‌ al.,‌‌(2021)‌‌applied‌‌the‌R‌ oR‌‌concept,‌‌citing‌‌a‌‌range‌‌of‌‌1.4–4.0‌‌from‌‌the‌‌literature‌‌reporting‌‌ upper(12%)/lower(7.8%)‌‌bounds‌‌for‌‌contribution‌‌of‌‌pRONO2‌‌ ‌to‌‌pNO3‌‌ ‌at‌‌a‌‌rural‌‌site‌‌in‌‌the‌‌North‌‌China‌‌ 205 Plains‌‌during‌‌summer.‌‌Kostenidou‌‌et‌‌al.‌‌(2015),‌‌on‌‌the‌‌other‌‌hand,‌‌estimated‌‌the‌R‌ p‌RONO2‌ ‌as‌‌the‌‌minimum‌‌ Ra‌mbient‌ ‌observed‌‌in‌‌ambient‌‌data‌‌during‌‌the‌‌campaigns,‌‌resulting‌‌in‌‌effective‌R‌ oRs‌ ‌‌of‌‌5.6‌‌and‌‌12‌‌for‌‌the‌‌ two‌‌campaigns‌‌investigated.‌‌The‌‌same‌‌method‌‌is‌‌used‌‌by‌‌Reyes-Villegas‌‌et‌‌al.‌‌(2018)‌‌(using‌‌46/30,‌‌and‌‌ resulting‌‌in‌‌an‌‌effective‌R‌ oR‌‌of‌‌5)‌‌and‌‌Florou‌‌et‌‌al.‌‌(2017)‌‌(resulting‌‌in‌‌high‌‌effective‌R‌ oRs‌ ‌‌of‌‌14‌‌and‌‌15‌‌ for‌‌the‌‌two‌‌campaigns‌‌investigated).‌‌Other‌‌field‌‌studies‌‌have‌‌followed‌‌the‌‌methods‌‌of‌‌Fry‌‌et‌‌al.‌‌(2013)‌‌ 210 (but‌‌using‌‌a‌‌few‌‌different‌‌fixed‌‌values‌‌for‌‌the‌R‌ oR)‌‌‌using‌‌HR‌‌data‌‌(Ayres‌‌et‌‌al.,‌‌2015;‌‌Fisher‌‌et‌‌al.,‌‌2016;‌‌
3‌‌Survey‌‌of‌‌NOx‌+‌ ‌ ‌ratios‌‌for‌‌particle-phase‌‌nitrates‌ ‌ Given‌‌the‌‌numerous‌‌applications‌‌of‌‌NOx‌+‌ ‌r‌ atios‌‌to‌‌separate‌‌pRONO2‌‌ ‌and‌‌NH4‌N‌ O3‌‌ ‌in‌‌AMS‌‌measurements,‌‌ 215 yet‌‌many‌‌variations‌‌in‌‌methods‌‌and‌‌the‌‌numerical‌‌values‌‌used‌‌within‌‌each‌‌method,‌‌we‌‌have‌‌conducted‌‌a‌‌ systematic‌‌survey‌‌of‌‌literature‌‌values‌‌and‌‌trends‌‌of‌‌NOx‌+‌ ‌ ‌ratios‌‌for‌‌different‌‌nitrates.‌‌Such‌‌data‌‌ compilation‌‌is‌‌aimed‌‌at‌‌evaluating‌‌the‌‌evidence‌‌that‌‌supports‌‌using‌‌a‌‌fixed‌R‌ oR‌‌to‌‌estimate‌R‌ p‌RONO2‌ ‌from‌‌ the‌‌calibration‌R‌ N‌ H4NO3‌ ‌and‌‌to‌‌investigate‌‌the‌‌variability‌‌in‌R‌ p‌RONO2‌ ‌produced‌‌from‌‌different‌‌sources.‌‌Figure‌‌ 1‌‌shows‌‌a‌‌compilation‌‌of‌R‌ oR‌‌values‌‌for‌‌pRONO2‌‌ ‌derived‌‌for‌‌chamber-generated‌‌SOA,‌‌isolated‌‌ 220 compounds‌‌(from‌‌chamber‌‌SOA‌‌or‌‌standards),‌‌and‌‌ambient‌‌measurements‌‌(using‌‌instrument‌‌comparisons‌‌ or‌‌PMF‌‌separation).‌‌Figure‌‌1‌‌also‌‌shows‌‌the‌R‌ oR‌‌for‌‌the‌‌same‌‌data‌‌as‌‌a‌‌histogram‌‌and‌‌average,‌‌as‌‌well‌‌as‌‌ the‌‌correlations‌‌of‌‌the‌‌pRONO2‌‌ ‌vs‌‌NH4‌N‌ O3‌‌ ‌(inverse)‌‌NOx‌+‌ ‌r‌ atios.‌‌Details‌‌of‌‌the‌‌values‌‌used‌‌to‌‌compute‌‌ the‌‌ratios‌‌and‌‌uncertainties,‌‌data‌‌sources,‌‌and‌‌any‌‌additional‌‌calculations‌‌for‌‌the‌‌information‌‌included‌‌in‌‌ Fig.‌‌1,‌‌are‌‌provided‌‌in‌‌Table‌‌S1.‌ ‌ 225 The‌‌correlation‌‌between‌‌the‌R‌ p‌RONO2‌ ‌and‌R‌ N‌ H4NO3‌ ‌is‌‌fairly‌‌strong‌‌(R2‌=‌ 0.54),‌‌considering‌‌the‌‌variety‌‌of‌‌ data‌‌sources‌‌and‌‌substantial‌‌measurement‌‌uncertainties.‌‌It‌‌provides‌‌strong‌‌evidence‌‌that,‌‌to‌‌first‌‌order,‌‌the‌‌ RoR‌‌method‌‌is‌‌consistent‌‌and‌‌supported‌‌by‌‌various‌‌methods,‌‌species/mixtures,‌‌instruments‌‌and‌‌operating‌‌ condit‌ions.‌‌The‌ ‌‌slopes‌‌of‌‌the‌‌linear‌‌regression‌‌constrained‌‌to‌‌a‌‌zero‌‌intercept‌‌using‌‌an‌‌ODR‌‌fit‌‌ (2.66±0.11;‌‌assuming‌‌both‌‌variables‌‌contribute‌‌comparable‌‌uncertainty)‌‌is‌‌equivalent‌‌to‌‌an‌‌overall‌R‌ oR‌‌ 230 and‌‌is‌‌similar‌‌to‌‌the‌‌average‌‌of‌‌the‌‌individual‌R‌ oR‌‌datapoints‌‌(mean±standard‌‌error:‌‌2.75±0.11).‌‌ Highlighted‌‌in‌‌the‌‌scatterplot‌‌in‌‌Fig.‌‌1‌‌are‌‌a‌‌couple‌‌of‌‌pairs‌‌of‌‌datapoints‌‌that‌‌are‌‌averages‌‌from‌‌several‌‌ experiments‌‌conducted‌‌in‌‌our‌‌laboratory‌‌with‌‌two‌‌different‌‌AMS‌‌during‌‌two‌‌different‌‌years,‌‌with‌‌ substantially‌‌different‌‌measured‌‌calibration‌R‌ N‌ H4NO3‌ ‌while‌‌sampling‌‌the‌‌same‌‌chamber‌‌SOA‌‌(see‌‌S1.2).‌‌ The‌‌trends‌‌in‌‌those‌‌points‌‌are‌‌similar‌‌to‌‌the‌‌overall‌‌trend‌‌and‌‌provide‌‌an‌‌example‌‌of‌‌the‌‌validity‌‌of‌‌the‌‌ 235 RoR‌‌method‌‌when‌‌only‌‌differences‌‌in‌‌instrument‌‌/‌‌operating‌‌conditions‌‌are‌‌present.‌‌Fig.‌‌S2‌‌shows‌‌a‌‌ complementary‌‌histogram‌‌to‌‌that‌‌in‌‌Fig.‌‌1‌‌for‌‌the‌R‌ p‌RONO2,‌ ‌‌without‌‌normalizing‌‌to‌R‌ N‌ H4NO3.‌ ‌‌Compared‌‌to‌‌ the‌‌normalized‌‌values‌‌shown‌‌in‌‌Fig.‌‌1‌‌(i.e.,‌‌RoRs‌ ),‌‌a‌‌factor‌‌of‌‌two‌‌larger‌‌relative‌‌variability‌‌is‌‌apparent,‌‌ with‌‌a‌‌relative‌‌standard‌‌deviation‌‌of‌‌49%‌‌compared‌‌to‌‌25%.‌‌Also‌‌of‌‌note‌‌is‌‌that‌‌the‌‌average‌‌value‌‌is‌‌ 0.21±0.10,‌‌twice‌‌as‌‌high‌‌as‌‌used‌‌in‌‌several‌‌literature‌‌studies.‌‌Finally,‌‌Fig.‌‌S3‌‌shows‌‌a‌‌complementary‌‌plot‌‌ 240 to‌‌the‌‌scatter‌‌plot‌‌in‌‌Fig.‌‌1,‌‌with‌‌the‌‌inverse‌‌NOx‌+‌ ‌ ‌ratios‌‌and‌‌axes‌‌swapped,‌‌which‌‌emphasizes‌‌different‌‌ data‌‌and‌‌outliers,‌‌and‌‌yields‌‌similar‌‌but‌‌slightly‌‌higher‌‌(

Summary

Introduction

170 sets‌‌where‌R‌ N‌ H4NO3‌ ‌was‌‌reported‌‌high”).‌‌Those‌‌authors‌‌state‌‌that‌‌their‌‌approach‌‌represents‌‌a‌‌lower‌‌limit‌‌of‌‌ pRONO2‌.‌ ‌‌Similarly,‌‌Brito‌‌et‌‌al.‌‌(2018),‌‌Schulz‌‌et‌‌al.‌‌(2018),‌‌Huang‌‌et‌‌al.‌‌(2019a,‌‌2019b),‌‌and‌‌Avery‌‌et‌‌al.‌‌ (2019),‌‌applied‌‌a‌‌fixed‌R‌ p‌RONO2‌ ‌of‌‌0.1‌‌(citing‌‌Kiendler-Sharr‌‌et‌‌al.‌‌(2016))‌‌for‌‌aircraft‌‌measurements‌‌in‌‌ West‌‌Africa,‌‌aircraft‌‌measurements‌‌in‌‌the‌‌Amazon,‌‌rural‌‌forest‌‌and‌‌urban‌‌sites‌‌in‌‌Germany,‌‌and‌‌seasonal‌‌ variations‌‌of‌‌indoor/outdoor‌‌air,‌‌respectively.‌‌The‌‌same‌‌method‌‌has‌‌been‌‌applied‌‌to‌‌laboratory‌‌studies‌‌of‌‌ They‌‌estimated‌‌lower‌‌(2.2)‌‌and‌‌upper‌‌(4.4)‌‌limits‌‌for‌R‌ oR‌‌(or‌R‌ p‌RONO2‌ ‌=‌‌0.1-0.2‌‌for‌‌their‌‌corresponding‌‌ RN‌ H4NO3)‌ ‌‌from‌‌literature‌‌reports‌‌of‌‌SOA‌‌formed‌‌from‌‌isoprene+NO3‌‌ ‌radicals‌‌(Bruns‌‌et‌‌al.,‌‌2010)‌‌and‌‌ β-pinene+NO3‌‌ ‌radicals‌‌(Fry‌‌et‌‌al.,‌‌2009;‌‌Bruns‌‌et‌‌al.,‌‌2010;‌‌Boyd‌‌et‌‌al.,‌‌2015),‌‌respectively.‌‌The‌‌rationale‌‌ 185 for‌‌their‌‌approach‌‌is‌‌that,‌‌for‌‌their‌‌region‌‌of‌‌study,‌‌those‌‌two‌‌BVOC‌‌may‌‌represent‌‌major‌‌contributions‌‌to‌‌ the‌‌mixture‌‌of‌‌pRONO2‌,‌ ‌‌and‌‌that‌‌the‌‌literature‌‌suggests‌‌there‌‌may‌‌be‌‌some‌‌source/composition‌‌ dependence‌‌of‌R‌ p‌RONO2.‌ ‌‌For‌‌the‌‌same‌‌region,‌‌Chen‌‌et‌‌al.‌‌(2020)‌‌used‌‌bounds‌‌of‌R‌ p‌RONO2‌(‌ 0‌ .1-0.2)‌,‌‌‌based‌‌on‌‌ similar‌‌logic,‌‌however‌‌not‌‌derived‌‌from‌‌a‌R‌ oR‌‌calculation‌‌(however‌‌equivalent‌‌to‌‌a‌R‌ oR‌‌of‌‌1.7-3.3).‌‌In‌‌a‌‌ study‌‌of‌‌pRONO2‌‌ ‌and‌‌SOA‌‌formation‌‌from‌‌Alberta‌‌oil‌‌sands‌‌extraction‌‌emissions‌‌from‌‌ground‌‌and‌‌ 190 aircraft‌‌measurements,‌‌Lee‌‌et‌‌al.‌‌(2019)‌‌used‌‌the‌‌same‌‌bounds‌‌of‌R‌ p‌RONO2‌(‌ 0‌ .1-0.2)‌,‌‌‌also‌‌not‌‌derived‌‌from‌‌ a‌R‌ oR‌‌calculation‌‌and‌‌citing‌‌Xu‌‌et‌‌al‌‌(2015a)‌‌and‌‌Farmer‌‌et‌‌al.‌‌(2010)‌‌(equivalent‌‌to‌‌a‌R‌ oR‌‌of‌‌1.4-2.9‌‌and‌‌ 1.5-3.0‌‌for‌‌the‌‌two‌‌datasets).‌‌The‌‌same‌‌methods‌‌as‌‌Xu‌‌et‌‌al.‌‌(2015a)‌‌were‌‌used‌‌(applying‌‌the‌‌same‌‌range‌‌ of‌R‌ oR)‌,‌‌for‌‌measurements‌‌conducted‌‌in‌‌Houston,‌‌TX‌‌(Dai‌‌et‌‌al.,‌‌2019)‌‌and‌‌the‌‌North‌‌China‌‌Plain‌‌(Xu‌‌et‌‌ al.,‌‌2021).‌‌However‌‌Xu‌‌et‌‌al.‌‌(2021)‌‌adjusted‌‌the‌R‌ N‌ H4NO3‌ ‌to‌‌match‌‌the‌‌highest‌‌NO2‌+‌ /‌ NO+‌‌ ‌ratios‌‌observed,‌‌ 195 since‌‌it‌‌was‌‌substantially‌‌higher‌‌than‌‌the‌‌calibration‌R‌ N‌ H4NO3‌ ‌(assuming‌‌for‌‌those‌‌periods,‌‌nitrate‌‌was‌‌ purely‌‌NH4‌N‌ O3‌)‌ .‌‌Thus,‌‌those‌‌five‌‌studies‌‌report‌‌their‌‌concentrations‌‌and‌‌inorganic/organic‌‌nitrate‌‌split‌‌ ,‌‌and‌‌report‌‌lower‌‌and‌‌upper‌‌bounds;‌‌however,‌‌Lee‌‌et‌‌al.,‌‌(2019)‌‌largely‌‌focused‌‌on‌‌results‌‌for‌‌ the‌‌upper‌‌limit‌‌pRONO2‌‌‌concentrations‌‌for‌‌the‌‌scientific‌‌analysis‌‌(with‌‌equivalent‌R‌ oRs:‌‌‌1.4/1.5).‌‌Zhou‌‌et‌‌ al.‌‌(2016),‌‌Zhu‌‌et‌‌al.‌‌(2016),‌‌and‌‌Yu‌‌et‌‌al.‌‌(2019)‌‌applied‌‌the‌R‌ oR‌‌concept,‌‌citing‌‌a‌‌range‌‌of‌‌2–4‌‌from‌‌the‌‌ 200 literature,‌‌and‌‌thus‌‌reported‌‌estimated‌‌lower/upper‌‌limit‌‌averages‌‌for‌‌contribution‌‌of‌‌pRONO2‌‌ ‌to‌‌pNO3‌‌ ‌in‌‌ New‌‌York‌‌City‌‌(summer,‌‌67%/95%),‌‌a‌‌background‌‌site‌‌in‌‌China‌‌(spring,‌‌15/22%),‌‌and‌‌an‌‌urban‌‌site‌‌in‌‌ China‌‌(during‌‌spring,‌‌13%/21%;‌‌summer,‌‌41%/64%;‌‌autumn,‌‌16%/25%),‌‌respectively.‌‌Similarly‌‌Zhu‌‌et‌‌ al.,‌‌(2021)‌‌applied‌‌the‌R‌ oR‌‌concept,‌‌citing‌‌a‌‌range‌‌of‌‌1.4–4.0‌‌from‌‌the‌‌literature‌‌reporting‌‌ upper(12%)/lower(7.8%)‌‌bounds‌‌for‌‌contribution‌‌of‌‌pRONO2‌‌ ‌to‌‌pNO3‌‌ ‌at‌‌a‌‌rural‌‌site‌‌in‌‌the‌‌North‌‌China‌‌ 205 Plains‌‌during‌‌summer.‌‌Kostenidou‌‌et‌‌al.‌‌(2015),‌‌on‌‌the‌‌other‌‌hand,‌‌estimated‌‌the‌R‌ p‌RONO2‌ ‌as‌‌the‌‌minimum‌‌ Ra‌mbient‌ ‌observed‌‌in‌‌ambient‌‌data‌‌during‌‌the‌‌campaigns,‌‌resulting‌‌in‌‌effective‌R‌ oRs‌ ‌‌of‌‌5.6‌‌and‌‌12‌‌for‌‌the‌‌ two‌‌campaigns‌‌investigated.‌‌The‌‌same‌‌method‌‌is‌‌used‌‌by‌‌Reyes-Villegas‌‌et‌‌al.‌‌(2018)‌‌(using‌‌46/30,‌‌and‌‌ resulting‌‌in‌‌an‌‌effective‌R‌ oR‌‌of‌‌5)‌‌and‌‌Florou‌‌et‌‌al.‌‌(2017)‌‌(resulting‌‌in‌‌high‌‌effective‌R‌ oRs‌ ‌‌of‌‌14‌‌and‌‌15‌‌ for‌‌the‌‌two‌‌campaigns‌‌investigated).‌‌Other‌‌field‌‌studies‌‌have‌‌followed‌‌the‌‌methods‌‌of‌‌Fry‌‌et‌‌al.‌‌(2013)‌‌ 210 (but‌‌using‌‌a‌‌few‌‌different‌‌fixed‌‌values‌‌for‌‌the‌R‌ oR)‌‌‌using‌‌HR‌‌data‌‌(Ayres‌‌et‌‌al.,‌‌2015;‌‌Fisher‌‌et‌‌al.,‌‌2016;‌‌

Results

Conclusion