Molecular insights into new particle formation across urban and polar environments

New particle formation (NPF) is a process involving formation of thermodynamically stable molecular clusters and their subsequent growth to larger sizes. NPF modulates the earth’s radiative budget and poses potentially significant health effects, however, the mechanisms driving NPF globally are still uncertain due to limited molecular scale measurements. Urban measurements in both Beijing and Barcelona show highly oxygenated multifunctional organic molecules in high mixing ratios, arising primarily from anthropogenic VOC precursors. Efficient autoxidation due to high temperatures is offset by rapid peroxy radical termination due to high NO$_x$ mixing ratios. Nucleation is seen to proceed by the nucleation of sulphuric acid, alkylamines, and HOMs in conjunction in Barcelona. An investigation of these mechanisms in the remote polar environment of the Antarctic Peninsula shows nucleation driven by sulphuric acid and amines, with elevations to both the sulphuric acid precursors and amines arising from the melt of sea ice. Particle formation rates are around two orders of magnitude more rapid in the urban environment than in the polar, and particle growth rates are around a single order of magnitude greater. This thesis demonstrates underappreciated roles of both anthropogenic VOC emissions in urban NPF and amine sources in polar regions in facilitating efficient NPF

Using lidar remote sensing and support vector machines to classify fire disturbance legacies in a Florida oak scrub landscape

Background/Question/Methods

Ecologists have long emphasized the reciprocal interactions between spatial pattern and ecological processes in the creation of landscape mosaics. While an enormous amount of recent research has focused on the quantification of spatial patterns, efforts to infer process from pattern have been hindered by the presence of multi-scale, often confounding, drivers of pattern in many landscapes. At the mesoscale, Holling’s extended keystone hypothesis posits that spatially contagious disturbances such as fire are the dominant pattern-generating processes. To test this hypothesis, we used fire history data and discrete, small-footprint lidar remote sensing acquired over a 22 sq. km landscape of oak scrub in the Kennedy Space Center/Merritt Island National Wildlife Refuge area on the east-central coast of Florida. We binned the lidar return data into 1 m vertical height intervals for each 5 m x 5 m horizontal cell. Since community structure tends to recover by 7 years post-fire, we tested for significant differences between recently-burned (< 7 years) and unburned (≥ 7 years) patches with multivariate analysis of variance. To predict the burn status of each cell, we then used distribution-free, nonlinear support vector machine (SVM) classifiers, which have proven to be highly accurate for complex pattern recognition problems.

Results/Conclusions 

We detected statistically significant differences in vegetation structure between burned and unburned patches for all of the dominant land cover types (upland non-forested, upland forested, wetland hardwood forest, and non-forested wetlands) in the study area. Initially, we obtained a predicted error rate of approximately 34% from the SVM classifier; by averaging the binned lidar data over a moving window of increasing size, however, we achieved substantial reductions in the predicted error rate for the SVM classifier. The optimal window size of 100 m x 100 m yielded a predicted misclassification rate of approximately 3%, an order of magnitude lower than the error rate obtained on the same data using a logistic regression classifier. These results suggest that, as predicted by the extended keystone hypothesis, fire disturbance is a dominant pattern-generating process at the patch scale in this oak scrub landscape. Furthermore, these results indicate that it is possible to use vertical vegetation structure, as represented by the binned lidar data, to predict burn status with a high level of accuracy. While our study employed a simple binary classification scheme, future research will focus on using SVM regression techniques to predict burn status with finer-grained classes of time since fire

Estimates of Future New Particle Formation under Different Emission Scenarios in Beijing

New particle formation (NPF) is a leading source of particulate matter by number and a contributor to particle mass during haze events. Reductions in emissions of air pollutants, many of which are NPF precursors, are expected in the move toward carbon neutrality or net-zero. Expected changes to pollutant emissions are used to investigate future changes to NPF processes, in comparison to a simulation of current conditions. The projected changes to SO2 emissions are key in changing future NPF number, with different scenarios producing either a doubling or near total reduction in sulfuric acid-amine particle formation rates. Particle growth rates are projected to change little in all but the strictest emission control scenarios. These changes will reduce the particle mass arising by NPF substantially, thus showing a further cobenefit of net-zero policies. Major uncertainties remain in future NPF including the volatility of oxygenated organic molecules resulting from changes to NOx and amine emissions

Integrating Land Cover Modeling and Adaptive Management to Conserve Endangered Species and Reduce Catastrophic Fire Risk

Land cover modeling is used to inform land management, but most often via a two-step process, where science informs how management alternatives can influence resources, and then, decision makers can use this information to make decisions. A more efficient process is to directly integrate science and decision-making, where science allows us to learn in order to better accomplish management objectives and is developed to address specific decisions. Co-development of management and science is especially productive when decisions are complicated by multiple objectives and impeded by uncertainty. Multiple objectives can be met by the specification of trade offs, and relevant uncertainty can be addressed through targeted science (i.e., models and monitoring). We describe how to integrate habitat and fuel monitoring with decision-making focused on the dual objectives of managing for endangered species and minimizing catastrophic fire risk. Under certain conditions, both objectives might be achieved by a similar management policy; other conditions require trade offs between objectives. Knowledge about system responses to actions can be informed by developing hypotheses based on ideas about fire behavior and then applying competing management actions to different land units in the same system state. Monitoring and management integration is important to optimize state-specific management decisions and to increase knowledge about system responses. We believe this approach has broad utility and identifies a clear role for land cover modeling programs intended to inform decision-making

Using Lidar-Derived Vegetation Profiles to Predict Time since Fire in an Oak Scrub Landscape in East-Central Florida

Disturbance plays a fundamental role in determining the vertical structure of vegetation in many terrestrial ecosystems, and knowledge of disturbance histories is vital for developing effective management and restoration plans. In this study, we investigated the potential of using vertical vegetation profiles derived from discrete-return lidar to predict time since fire (TSF) in a landscape of oak scrub in east-central Florida. We predicted that fire influences vegetation structure at the mesoscale (i.e., spatial scales of tens of meters to kilometers). To evaluate this prediction, we binned lidar returns into 1m vertical by 5 x 5 m horizontal cells and averaged the resulting profiles over a range of horizontal window sizes (0 to 500 m on a side). We then performed a series of resampling tests to compare the performance of support vector machine (SVM), k-nearest neighbor (k-NN), logistic regression, and linear discriminant analysis (LDA) classifiers and to estimate the amount of training data necessary to achieve satisfactory performance. Our results indicate that: (1) the SVMs perform significantly better than the other classifiers, (2) SVM classifiers may require relatively small training data sets, and (3) the highest classification accuracies occur with averaging over windows representing sizes in the mesoscale range

The Effects of Vegetative Type, Edges, Fire History, Rainfall and Management in Fire-Maintained Ecosystems

The combined effects of repeated fires, climate, and landscape features (e.g., edges) need greater focus in fire ecology studies, which usually emphasize characteristics of the most recent fire and not fire history. Florida scrub-jays are an imperiled, territorial species that prefer medium (1.2-1.7 m) shrub heights. We measured short, medium, and tall habitat quality states annually within 10 ha grid cells that represented potential territories because frequent fires and vegetative recovery cause annual variation in habitat quality. We used multistate models and model selection to test competing hypotheses about how transition probabilities between states varied annually as functions of environmental covariates. Covariates included vegetative type, edges, precipitation, openings (gaps between shrubs), mechanical cutting, and fire characteristics. Fire characteristics not only included an annual presenceabsence of fire covariate, but also fire history covariates: time since the previous fire, the maximum fire-free interval, and the number of repeated fires. Statistical models with support included many covariates for each transition probability, often including fire history, interactions and nonlinear relationships. Tall territories resulted from 28 years of fire suppression and habitat fragmentation that reduced the spread of fires across landscapes. Despite 35 years of habitat restoration and prescribed fires, half the territories remained tall suggesting a regime shift to a less desirable habitat condition. Measuring territory quality states and environmental covariates each year combined with multistate modeling provided a useful empirical approach to quantify the effects of repeated fire in combinations with environmental variables on transition probabilities that drive management strategies and ecosystem change

The Effects of Vegetative Type, Edges, Fire History, Rainfall and Management in Fire-Maintained Habitat

The combined effects of fire history, climate, and landscape features (e.g., edges) on habitat specialists need greater focus in fire ecology studies, which usually only emphasize characteristics of the most recent fire. Florida scrub-jays are an imperiled, territorial species that prefer medium (1.2-1.7 m) shrub heights, which are dynamic because of frequent fires. We measured short, medium, and tall habitat quality states annually within 10 ha grid cells (that represented potential territories) because fires and vegetative recovery cause annual variation in habitat quality. We used multistate models and model selection to test competing hypotheses about how transition probabilities vary between states as functions of environmental covariates. Covariates included vegetative type, edges (e.g., roads, forests), precipitation, openings (gaps between shrubs), mechanical cutting, and fire characteristics. Fire characteristics not only included an annual presence/absence of fire covariate, but also fire history covariates: time since the previous fire, the longest fire-free interval, and the number of repeated fires. Statistical models with support included many covariates for each transition probability, often including fire history, interactions and nonlinear relationships. Tall territories resulted from 28 years of fire suppression and habitat fragmentation that reduced the spread of fires across landscapes. Despite 35 years of habitat restoration and prescribed fires, half the territories remained tall suggesting a regime shift to a less desirable habitat condition. Edges reduced the effectiveness of fires in setting degraded scrub and flatwoods into earlier successional states making mechanical cutting an important tool to compliment frequent prescribed fires

Direct Measurements of Covalently Bonded Sulfuric Anhydrides from Gas-Phase Reactions of SO₃ with Acids under Ambient Conditions

Sulfur trioxide (SO3) is an important oxide of sulfur and a key intermediate in the formation of sulfuric acid (H2SO4, SA) in the Earth’s atmosphere. This conversion to SA occurs rapidly due to the reaction of SO3 with a water dimer. However, gas-phase SO3 has been measured directly at concentrations that are comparable to that of SA under polluted mega-city conditions, indicating gaps in our current understanding of the sources and fates of SO3. Its reaction with atmospheric acids could be one such fate that can have significant implications for atmospheric chemistry. In the present investigation, laboratory experiments were conducted in a flow reactor to generate a range of previously uncharacterized condensable sulfur-containing reaction products by reacting SO3 with a set of atmospherically relevant inorganic and organic acids at room temperature and atmospheric pressure. Specifically, key inorganic acids known to be responsible for most ambient new particle formation events, iodic acid (HIO3, IA) and SA, are observed to react promptly with SO3 to form iodic sulfuric anhydride (IO3SO3H, ISA) and disulfuric acid (H2S2O7, DSA). Carboxylic sulfuric anhydrides (CSAs) were observed to form by the reaction of SO3 with C2 and C3 monocarboxylic (acetic and propanoic acid) and dicarboxylic (oxalic and malonic acid)-carboxylic acids. The formed products were detected by a nitrate-ion-based chemical ionization atmospheric pressure interface time-of-flight mass spectrometer (NO3--CI-APi-TOF; NO3--CIMS). Quantum chemical methods were used to compute the relevant SO3 reaction rate coefficients, probe the reaction mechanisms, and model the ionization chemistry inherent in the detection of the products by NO3--CIMS. Additionally, we use NO3--CIMS ambient data to report that significant concentrations of SO3 and its acid anhydride reaction products are present under polluted, marine and polar, and volcanic plume conditions. Considering that these regions are rich in the acid precursors studied here, the reported reactions need to be accounted for in the modeling of atmospheric new particle formation.</p

Road Traffic Emissions Lead to Much Enhanced New Particle Formation through Increased Growth Rates

New particle formation (NPF) is a major source of atmospheric aerosol particles, including cloud condensation nuclei (CCN), by number globally. Previous research has highlighted that NPF is less frequent but more intense at roadsides compared to urban background. Here, we closely examine NPF at both background and roadside sites in urban Central Europe. We show that the concentration of oxygenated organic molecules (OOMs) is greater at the roadside, and the condensation of OOMs along with sulfuric acid onto new particles is sufficient to explain the growth at both sites. We identify a hitherto unreported traffic-related OOM source contributing 29% and 16% to total OOMs at the roadside and background, respectively. Critically, this hitherto undiscovered OOM source is an essential component of urban NPF. Without their contribution to growth rates and the subsequent enhancements to particle survival, the number of &gt;50 nm particles produced by NPF would be reduced by a factor of 21 at the roadside site. Reductions to hydrocarbon emissions from road traffic may thereby reduce particle numbers and CCN counts.</p