Particle-phase accretion forms dimer esters in pinene secondary organic aerosol.

Secondary organic aerosol (SOA) is ubiquitous in the atmosphere and plays a pivotal role in climate, air quality, and health. The production of low-volatility dimeric compounds through accretion reactions is a key aspect of SOA formation. However, despite extensive study, the structures and thus the formation mechanisms of dimers in SOA remain largely uncharacterized. In this work, we elucidate the structures of several major dimer esters in SOA from ozonolysis of α-pinene and ß-pinene-substantial global SOA sources-through independent synthesis of authentic standards. We show that these dimer esters are formed in the particle phase and propose a mechanism of nucleophilic addition of alcohols to a cyclic acylperoxyhemiacetal. This chemistry likely represents a general pathway to dimeric compounds in ambient SOA.

Methane Emissions from Dairy Operations in California's San Joaquin Valley Evaluated Using Airborne Flux Measurements.

State inventories indicate that dairy operations account for nearly half of California's methane budget. Recent analyses suggest, however, that these emissions may be underestimated, complicating efforts to develop emission reduction strategies. Here, we report estimates of dairy methane emissions in the southern San Joaquin Valley (SJV) of California in June 2021 using airborne flux measurements. We find average dairy methane fluxes of 512 ± 178 mg m-2 h-1 from a region of 300+ dairies near Visalia, CA using a combination of eddy covariance and mass balance-based techniques, corresponding to 118 ± 41 kg dairy-1 h-1. These values estimated during our June campaign are 39 ± 48% larger than annual average estimates from the recently developed VISTA-CA inventory. We observed notable increases in emissions with temperature. Our estimates align well with inventory predictions when parametrizations for the temperature dependence of emissions are applied. Our measurements further demonstrate that the VISTA-CA emission inventory is considerably more accurate than the EPA GHG-I inventory in this region. Source apportionment analyses confirm that dairy operations produce the majority of methane emissions in the southern SJV (∼65%). Fugitive oil and gas (O&G) sources account for the remaining ∼35%. Our results support the accuracy of the process-based models used to develop dairy emission inventories and highlight the need for additional investigation of the meteorological dependence of these emissions.

Comparison between Spatially Resolved Airborne Flux Measurements and Emission Inventories of Volatile Organic Compounds in Los Angeles.

Los Angeles is a major hotspot for ozone and particulate matter air pollution in the United States. Ozone and PM2.5 in this region have not improved substantially for the past decade, despite a reduction in vehicular emissions of their precursors, NOx and volatile organic compounds (VOCs). This reduction in "traditional" sources has made the current emission mixture of air pollutant precursors more uncertain. To map and quantify emissions of a wide range of VOCs in this urban area, we performed airborne eddy covariance measurements with wavelet analysis. VOC fluxes measured include tracers for source categories, such as traffic, vegetation, and volatile chemical products (VCPs). Mass fluxes were dominated by oxygenated VOCs, with ethanol contributing ∼29% of the total. In terms of OH reactivity and aerosol formation potential, terpenoids contributed more than half. Observed fluxes were compared with two commonly used emission inventories: the California Air Resources Board inventory and the combination of the Biogenic Emission Inventory System with the Fuel-based Inventory of Vehicle Emissions combined with Volatile Chemical Products (FIVE-VCP). The comparison shows mismatches regarding the amount, spatial distribution, and weekend effects of observed VOC emissions with the inventories. The agreement was best for typical transportation related VOCs, while discrepancies were larger for biogenic and VCP-related VOCs.

Overview of ICARUS-A Curated, Open Access, Online Repository for Atmospheric Simulation Chamber Data.

Atmospheric simulation chambers continue to be indispensable tools for research in the atmospheric sciences. Insights from chamber studies are integrated into atmospheric chemical transport models, which are used for science-informed policy decisions. However, a centralized data management and access infrastructure for their scientific products had not been available in the United States and many parts of the world. ICARUS (Integrated Chamber Atmospheric data Repository for Unified Science) is an open access, searchable, web-based infrastructure for storing, sharing, discovering, and utilizing atmospheric chamber data [https://icarus.ucdavis.edu]. ICARUS has two parts: a data intake portal and a search and discovery portal. Data in ICARUS are curated, uniform, interactive, indexed on popular search engines, mirrored by other repositories, version-tracked, vocabulary-controlled, and citable. ICARUS hosts both legacy data and new data in compliance with open access data mandates. Targeted data discovery is available based on key experimental parameters, including organic reactants and mixtures that are managed using the PubChem chemical database, oxidant information, nitrogen oxide (NOx) content, alkylperoxy radical (RO2) fate, seed particle information, environmental conditions, and reaction categories. A discipline-specific repository such as ICARUS with high amounts of metadata works to support the evaluation and revision of atmospheric model mechanisms, intercomparison of data and models, and the development of new model frameworks that can have more predictive power in the current and future atmosphere. The open accessibility and interactive nature of ICARUS data may also be useful for teaching, data mining, and training machine learning models.

Widespread missing super-emitters of nitrogen oxides across China inferred from year-round satellite observations.

Nitrogen oxides (NOx ≡ NO + NO2) play a central role in air pollution and are targeted for emission mitigation by environmental protection agencies globally. Unique challenges for mitigation are presented by super-emitters, typically with the potential to dominate localized NOx budgets. Nevertheless, identifying super-emitters still challenges emission mitigation, while the spatial resolution of emission monitoring rises continuously. Here we develop an efficient, super-resolution (1 × 1 km2) inverse model based on year-round TROPOMI satellite observations over China. Consequently, we resolve hundreds of super-emitters in virtually every corner of China, even in remote and mountainous areas. They are attributed to individual plants or parks, mostly associated with industrial sectors, like energy, petrochemical, and iron and steel industries. State-of-the-art bottom-up emission estimates (i.e., MEICv1.3 and HTAPv2), as well as classic top-down inverse methods (e.g., a CTM coupled with the Ensemble Kalman Filter), do not adequately identify these super-emitters. Remarkably, more than one hundred super-emitters are unambiguously missed, while the establishments or discontinuations of the super-emitters potentially lead to under- or over-estimates, respectively. Moreover, evidence shows that these super-emitters generally dominate the NOx budget in a localized area (e.g., equivalent to a spatial scale of a medium-sized county). Although our dataset is incomplete nationwide due to the undetectable super-emitters on top of high pollution, our results imply that super-emitters contribute significantly to national NOx budgets and thus suggest the necessity to address the NOx budget by revisiting super-emitters on a large scale. Integrating the results we obtain here with a multi-tiered observation system can lead to identification and mitigation of anomalous NOx emissions.

Vapors Are Lost to Walls, Not to Particles on the Wall: Artifact-Corrected Parameters from Chamber Experiments and Implications for Global Secondary Organic Aerosol.

Atmospheric models of secondary organic aerosol (OA) (SOA) typically rely on parameters derived from environmental chambers. Chambers are subject to experimental artifacts, including losses of (1) particles to the walls (PWL), (2) vapors to the particles on the wall (V2PWL), and (3) vapors to the wall directly (VWL). We present a method for deriving artifact-corrected SOA parameters and translating these to volatility basis set (VBS) parameters for use in chemical transport models (CTMs). Our process involves combining a box model that accounts for chamber artifacts (Statistical Oxidation Model with a TwO-Moment Aerosol Sectional model (SOM-TOMAS)) with a pseudo-atmospheric simulation to develop VBS parameters that are fit across a range of OA mass concentrations. We found that VWL led to the highest percentage change in chamber SOA mass yields (high NOx: 36-680%; low NOx: 55-250%), followed by PWL (high NOx: 8-39%; low NOx: 10-37%), while the effects of V2PWL are negligible. In contrast to earlier work that assumed that V2PWL was a meaningful loss pathway, we show that V2PWL is an unimportant SOA loss pathway and can be ignored when analyzing chamber data. Using our updated VBS parameters, we found that not accounting for VWL may lead surface-level OA to be underestimated by 24% (0.25 µg m-3) as a global average or up to 130% (9.0 µg m-3) in regions of high biogenic or anthropogenic activity. Finally, we found that accurately accounting for PWL and VWL improves model-measurement agreement for fine mode aerosol mass concentrations (PM2.5) in the GEOS-Chem model.

Notable impact of wildfires in the western United States on weather hazards in the central United States.

Increased wildfire events constitute a significant threat to life and property in the United States. Wildfire impact on severe storms and weather hazards is another pathway that threatens society, and our understanding of which is very limited. Here, we use unique modeling developments to explore the effects of wildfires in the western US (mainly California and Oregon) on precipitation and hail in the central US. We find that the western US wildfires notably increase the occurrences of heavy precipitation rates by 38% and significant severe hail (≥2 in.) by 34% in the central United States. Both heat and aerosols from wildfires play an important role. By enhancing surface high pressure and increasing westerly and southwesterly winds, wildfires in the western United States produce (1) stronger moisture and aerosol transport to the central United States and (2) larger wind shear and storm-relative helicity in the central United States. Both the meteorological environment more conducive to severe convective storms and increased aerosols contribute to the enhancements of heavy precipitation rates and large hail. Moreover, the local wildfires in the central US also enhance the severity of storms, but their impact is notably smaller than the impact of remote wildfires in California and Oregon because of the lessened severity of the local wildfires. As wildfires are projected to be more frequent and severe in a warmer climate, the influence of wildfires on severe weather in downwind regions may become increasingly important.

Heterogeneous iodine-organic chemistry fast-tracks marine new particle formation.

The gas-phase formation of new particles less than 1 nm in size and their subsequent growth significantly alters the availability of cloud condensation nuclei (CCN, >30-50 nm), leading to impacts on cloud reflectance and the global radiative budget. However, this growth cannot be accounted for by condensation of typical species driving the initial nucleation. Here, we present evidence that nucleated iodine oxide clusters provide unique sites for the accelerated growth of organic vapors to overcome the coagulation sink. Heterogeneous reactions form low-volatility organic acids and alkylaminium salts in the particle phase, while further oligomerization of small α-dicarbonyls (e.g., glyoxal) drives the particle growth. This identified heterogeneous mechanism explains the occurrence of particle production events at organic vapor concentrations almost an order of magnitude lower than those required for growth via condensation alone. A notable fraction of iodine associated with these growing particles is recycled back into the gas phase, suggesting an effective transport mechanism for iodine to remote regions, acting as a "catalyst" for nucleation and subsequent new particle production in marine air.

Quantifying on-road vehicle emissions during traffic congestion using updated emission factors of light-duty gasoline vehicles and real-world traffic monitoring big data.

Light-duty gasoline vehicles (LDGVs) have made up >90 % of vehicle fleets in China since 2019, moreover, with a high annual growth rate (> 10 %) since 2017. Hence, accurate estimates of air pollutant emissions of these fast-changing LDGVs are vital for air quality management, human healthcare, and ecological protection. However, this issue is poorly quantified due to insufficient reserves of timely updated LDGV emission factors, which are dependent on real-world activity levels. Here we constructed a big dataset of explicit emission profiles (e.g., emission factors and accumulated mileages) for 159,051 LDGVs based on an official I/M database by matching real-time traffic dynamics via real-world traffic monitoring (e.g., traffic volumes and speeds). Consequently, we provide robust evidence that the emission factors of these LDGVs follow a clear heavy-tailed distribution. The top 10 % emitters contributed >60 % to the total fleet emissions, while the bottom 50 % contributed <10 %. Such emission factors were effectively reduced by 75.7-86.2 % as official emission standards upgraded gradually (i.e., from China 2 to China 5) within 13 years from 2004 to 2017. Nevertheless, such achievements would be offset once traffic congestion occurred. In the real world, the typical traffic congestions (i.e., vehicle speed <5 km/h) can lead to emissions 5- 9 times higher than those on non-congested roads (i.e., vehicle speed >50 km/h). These empirical analyses enabled us to propose future traffic scenarios that could harmonize emission standards and traffic congestion. Practical approaches on vehicle emission controls under realistic conditions are proposed, which would provide new insights for future urban vehicle emission management.

Process-Level Modeling Can Simultaneously Explain Secondary Organic Aerosol Evolution in Chambers and Flow Reactors.

Secondary organic aerosol (SOA) data gathered in environmental chambers (ECs) have been used extensively to develop parameters to represent SOA formation and evolution. The EC-based parameters are usually constrained to less than one day of photochemical aging but extrapolated to predict SOA aging over much longer timescales in atmospheric models. Recently, SOA has been increasingly studied in oxidation flow reactors (OFRs) over aging timescales of one to multiple days. However, these OFR data have been rarely used to validate or update the EC-based parameters. The simultaneous use of EC and OFR data is challenging because the processes relevant to SOA formation and evolution proceed over very different timescales, and both reactor types exhibit distinct experimental artifacts. In this work, we show that a kinetic SOA chemistry and microphysics model that accounts for various processes, including wall losses, aerosol phase state, heterogeneous oxidation, oligomerization, and new particle formation, can simultaneously explain SOA evolution in EC and OFR experiments, using a single consistent set of SOA parameters. With α-pinene as an example, we first developed parameters by fitting the model output to the measured SOA mass concentration and oxygen-to-carbon (O:C) ratio from an EC experiment (<1 day of aging). We then used these parameters to simulate SOA formation in OFR experiments and found that the model overestimated SOA formation (by a factor of 3-16) over photochemical ages ranging from 0.4 to 13 days, when excluding the abovementioned processes. By comprehensively accounting for these processes, the model was able to explain the observed evolution in SOA mass, composition (i.e., O:C), and size distribution in the OFR experiments. This work suggests that EC and OFR SOA data can be modeled consistently, and a synergistic use of EC and OFR data can aid in developing more refined SOA parameters for use in atmospheric models.

A Neural Network-Assisted Euler Integrator for Stiff Kinetics in Atmospheric Chemistry.

Atmospheric chemistry, characterized by highly coupled sets of ordinary differential equations (ODEs), is dynamically stiff owing to the fact that both fast and slow processes exist simultaneously. We develop here a neural network-assisted Euler integrator for the kinetics of atmospheric chemical reactions. We show that the integral kernel of the chemical reaction system can be represented by a neural network. The stiff kinetics of the atmospheric H2O2/OH/HO2 system, involving 3 species and 4 reactions, and a simplified air pollution mechanism, involving 20 species and 25 reactions, are developed here in detail as illustrations of the neural network Euler integrator. The algorithm developed accelerates the numerical integration of large sets of coupled stiff ODEs by at least one order of magnitude by avoiding the intensive linear algebra that is required in traditional stiff ODE solvers; moreover, the mechanism-specific neural network-assisted algorithm can be readily coupled to other modules in a three-dimensional atmospheric chemical transport model.

Rapid assessments of light-duty gasoline vehicle emissions using on-road remote sensing and machine learning.

In-time and accurate assessments of on-road vehicle emissions play a central role in urban air quality and health policymaking. However, official insight is hampered by the Inspection/Maintenance (I/M) procedure conducted in the laboratory annually. It not only has a large gap to real-world situations (e.g., meteorological conditions) but also is incapable of regular supervision. Here we build a unique dataset including 103,831 light-duty gasoline vehicles, in which on-road remote sensing (ORRS) measurements are linked to the I/M records based on the vehicle identification numbers and license plates. On this basis, we develop an ensemble model framework that integrates three machining learning algorithms, including neural network (NN), extreme gradient boosting (XGBoost), and random forest (RF). We demonstrate that this ensemble model could rapidly assess the vehicle-specific emissions (i.e., CO, HC, and NO). In particular, the model performs quite well for the passing vehicles under normal conditions (i.e., lower VSP (<18 kw/t), temperature (6-32 °C), relative humidity (<80%), and wind speed (<5 m/s)). Together with the current emission standard, we identify a large number of the 'dirty' (2.33%) or 'clean' (74.92%) vehicles in the real world. Our results show that the ORRS measurements, assisted by the machine-learning-based ensemble model developed here, can realize day-to-day supervision of on-road vehicle-specific emissions. This approach framework provides a valuable opportunity to reform the I/M procedures globally and mitigate urban air pollution deeply.

Multigeneration Production of Secondary Organic Aerosol from Toluene Photooxidation.

Photooxidation of volatile organic compounds (VOCs) produces secondary organic aerosol (SOA) and light-absorbing brown carbon (BrC) via multiple reaction steps/pathways, reflecting significant chemical complexity relevant to gaseous oxidation and subsequent gas-to-particle conversion. Toluene is an important VOC under urban conditions, but the fundamental chemical mechanism leading to SOA formation remains uncertain. Here, we elucidate multigeneration SOA production from toluene by simultaneously tracking the evolutions of gas-phase oxidation and aerosol formation in a reaction chamber. Large size increase and browning of monodisperse sub-micrometer seed particles occur shortly after initiating oxidation by hydroxyl radical (OH) at 10-90% relative humidity (RH). The evolution in gaseous products and aerosol properties (size/density/optical properties) and chemical speciation of aerosol-phase products indicate that the aerosol growth and browning result from earlier generation products consisting dominantly of dicarbonyl and carboxylic functional groups. While volatile dicarbonyls engage in aqueous reactions to yield nonvolatile oligomers and light-absorbing nitrogen heterocycles/heterochains (in the presence of NH3) at high RH, organic acids contribute to aerosol carboxylates via ionic dissociation or acid-base reaction in a wide RH range. We conclude that toluene contributes importantly to SOA/BrC formation from dicarbonyls and organic acids because of their prompt and high yields from photooxidation and unique functionalities for participation in aerosol-phase reactions.

From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model.

The large fluctuations in traffic during the COVID-19 pandemic provide an unparalleled opportunity to assess vehicle emission control efficacy. Here we develop a random-forest regression model, based on the large volume of real-time observational data during COVID-19, to predict surface-level NO2, O3, and fine particle concentration in the Los Angeles megacity. Our model exhibits high fidelity in reproducing pollutant concentrations in the Los Angeles Basin and identifies major factors controlling each species. During the strictest lockdown period, traffic reduction led to decreases in NO2 and particulate matter with aerodynamic diameters <2.5 µm by -30.1% and -17.5%, respectively, but a 5.7% increase in O3 Heavy-duty truck emissions contribute primarily to these variations. Future traffic-emission controls are estimated to impose similar effects as observed during the COVID-19 lockdown, but with smaller magnitude. Vehicular electrification will achieve further alleviation of NO2 levels.

Unexpected Oligomerization of Small α-Dicarbonyls for Secondary Organic Aerosol and Brown Carbon Formation.

Large amounts of small α-dicarbonyls (glyoxal and methylglyoxal) are produced in the atmosphere from photochemical oxidation of biogenic isoprene and anthropogenic aromatics, but the fundamental mechanisms leading to secondary organic aerosol (SOA) and brown carbon (BrC) formation remain elusive. Methylglyoxal is commonly believed to be less reactive than glyoxal because of unreactive methyl substitution, and available laboratory measurements showed negligible aerosol growth from methylglyoxal. Herein, we present experimental results to demonstrate striking oligomerization of small α-dicarbonyls leading to SOA and BrC formation on sub-micrometer aerosols. Significantly more efficient growth and browning of aerosols occur upon exposure to methylglyoxal than glyoxal under atmospherically relevant concentrations and in the absence/presence of gas-phase ammonia and formaldehyde, and nonvolatile oligomers and light-absorbing nitrogen-heterocycles are identified as the dominant particle-phase products. The distinct aerosol growth and light absorption are attributed to carbenium ion-mediated nucleophilic addition, interfacial electric field-induced attraction, and synergetic oligomerization involving organic/inorganic species, leading to surface- or volume-limited reactions that are dependent on the reactivity and gaseous concentrations. Our findings resolve an outstanding discrepancy concerning the multiphase chemistry of small α-dicarbonyls and unravel a new avenue for SOA and BrC formation from atmospherically abundant, ubiquitous carbonyls and ammonia/ammonium sulfate.

Emissions Measurements from Household Solid Fuel Use in Haryana, India: Implications for Climate and Health Co-benefits.

A large concern with estimates of climate and health co-benefits of "clean" cookstoves from controlled emissions testing is whether results represent what actually happens in real homes during normal use. A growing body of evidence indicates that in-field emissions during daily cooking activities differ substantially from values obtained in laboratories, with correspondingly different estimates of co-benefits. We report PM2.5 emission factors from uncontrolled cooking (n = 7) and minimally controlled cooking tests (n = 51) using traditional chulha and angithi stoves in village kitchens in Haryana, India. Minimally controlled cooking tests (n = 13) in a village kitchen with mixed dung and brushwood fuels were representative of uncontrolled field tests for fine particulate matter (PM2.5), organic and elemental carbon (p > 0.5), but were substantially higher than previously published water boiling tests using dung or wood. When the fraction of nonrenewable biomass harvesting, elemental, and organic particulate emissions and modeled estimates of secondary organic aerosol (SOA) are included in 100 year global warming commitments (GWC100), the chulha had a net cooling impact using mixed fuels typical of the region. Correlation between PM2.5 emission factors and GWC (R2 = 0.99) implies these stoves are climate neutral for primary PM2.5 emissions of 8.8 ± 0.7 and 9.8 ± 0.9 g PM2.5/kg dry fuel for GWC20 and GWC100, respectively, which is close to the mean for biomass stoves in global emission inventories.

City-level air quality improvement in the Beijing-Tianjin-Hebei region from 2016/17 to 2017/18 heating seasons: Attributions and process analysis.

With the implementation of clean air strategies, PM2.5 pollution abatement has been observed in the "2 + 26" cities in the Beijing-Tianjin-Hebei (BTH) region (referred to as the BTH2+26) and their surrounding areas. To identify the drivers for PM2.5 concentration decreases in the BTH2+26 cites from the 2016/17 heating season (HS1617) to the 2017/18 heating season (HS1718), we investigated the contributions of meteorological conditions and emission-reduction measures by Community Multi-Scale Air Quality (CMAQ) model simulations. The source apportionments of five sector sources (i.e., agriculture, industry, power plants, traffic and residential), and regional sources (i.e., local, within-BTH: other cities within the BTH2+26 cities, outside-BTH, and boundary conditions (BCON)) to the PM2.5 decreases in the BTH2+26 cities were estimated with the Integrated Source Apportionment Method (ISAM). Mean PM2.5 concentrations in the BTH2+26 cities substantially decreased from 77.4 to 152.5 µg m-3 in HS1617 to 52.9-101.9 µg m-3 in HS1718, with the numbers of heavy haze (daily PM2.5 ≥150 µg m-3) days decreasing from 17-77 to 5-30 days. The model simulation results indicated that the PM2.5 concentration decreases in most of the BTH2+26 cities were attributed to emission reductions (0.4-55.0 µg m-3, 2.3-81.6% of total), but the favorable meteorological conditions also played important roles (1.9-25.4 µg m-3, 18.4-97.7%). Residential sources dominated the PM2.5 reductions, leading to decreases in average PM2.5 concentrations by more than 30 µg m-3 in severely polluted cities (i.e., Shijiazhuang, Baoding, Xingtai, and Beijing). Regional source analyses showed that both local and within-BTH sources were significant contributors to PM2.5 concentrations for most cities. Emission controls in local and within-BTH sources in HS1718 decreased the average PM2.5 concentrations by 0.1-47.2 µg m-3 and 0.3-22.1 µg m-3, respectively, relative to those in HS1617. Here we demonstrate that a combination of favorable meteorological conditions and anthropogenic emission reductions contributed to the improvement of air quality from HS1617 to HS1718 in the BTH2+26 cities.

Modeling secondary organic aerosol formation from volatile chemical products.

Volatile chemical products (VCPs) are commonly-used consumer and industrial items that are an important source of anthropogenic emissions. Organic compounds from VCPs evaporate on atmospherically relevant time scales and include many species that are secondary organic aerosol (SOA) precursors. However, the chemistry leading to SOA, particularly that of intermediate volatility organic compounds (IVOCs), has not been fully represented in regional-scale models such as the Community Multiscale Air Quality (CMAQ) model, which tend to underpredict SOA concentrations in urban areas. Here we develop a model to represent SOA formation from VCP emissions. The model incorporates a new VCP emissions inventory and employs three new classes of emissions: siloxanes, oxygenated IVOCs, and nonoxygenated IVOCs. VCPs are estimated to produce 1.67 µg m-3 of noontime SOA, doubling the current model predictions and reducing the SOA mass concentration bias from -75% to -58% when compared to observations in Los Angeles in 2010. While oxygenated and nonoxygenated intermediate volatility VCP species are emitted in similar quantities, SOA formation is dominated by the nonoxygenated IVOCs. Formaldehyde and SOA show similar relationships to temperature and bias signatures indicating common sources and/or chemistry. This work suggests that VCPs contribute up to half of anthropogenic SOA in Los Angeles and models must better represent SOA precursors from VCPs to predict the urban enhancement of SOA.

On the relationship between cloud water composition and cloud droplet number concentration.

Aerosol-cloud interactions are the largest source of uncertainty in quantifying anthropogenic radiative forcing. The large uncertainty is, in part, due to the difficulty of predicting cloud microphysical parameters, such as the cloud droplet number concentration (N d). Even though rigorous first-principle approaches exist to calculate N d, the cloud and aerosol research community also relies on empirical approaches such as relating N d to aerosol mass concentration. Here we analyze relationships between N d and cloud water chemical composition, in addition to the effect of environmental factors on the degree of the relationships. Warm, marine, stratocumulus clouds off the California coast were sampled throughout four summer campaigns between 2011 and 2016. A total of 385 cloud water samples were collected and analyzed for 80 chemical species. Single- and multispecies log-log linear regressions were performed to predict N d using chemical composition. Single-species regressions reveal that the species that best predicts N d is total sulfate ( R adj 2 = 0.40 ). Multispecies regressions reveal that adding more species does not necessarily produce a better model, as six or more species yield regressions that are statistically insignificant. A commonality among the multispecies regressions that produce the highest correlation with N d was that most included sulfate (either total or non-sea-salt), an ocean emissions tracer (such as sodium), and an organic tracer (such as oxalate). Binning the data according to turbulence, smoke influence, and in-cloud height allowed for examination of the effect of these environmental factors on the composition-N d correlation. Accounting for turbulence, quantified as the standard deviation of vertical wind speed, showed that the correlation between N d with both total sulfate and sodium increased at higher turbulence conditions, consistent with turbulence promoting the mixing between ocean surface and cloud base. Considering the influence of smoke significantly improved the correlation with N d for two biomass burning tracer species in the study region, specifically oxalate and iron. When binning by in-cloud height, non-sea-salt sulfate and sodium correlated best with N d at cloud top, whereas iron and oxalate correlated best with N d at cloud base.