Stability of Single Particle Tracers for Differentiating Between Heavy- and Light-Duty Vehicle Emissions

To determine the size and chemical composition of particles derived from on-road vehicle emissions, individual particles were sampledcontinuously with an aerosol time-of-flight mass spectrometer (ATOFMS) at the Caldecott Tunnel in Northern California. In this tunnel, traffic is segregated, such that in theory only light duty vehicle emissions or a mix of heavy- (HDV) and light-duty vehicle (LDV) emissions can be sampled separately. Two studies were carried out, one in November 1997 anda secondin July 2000, time periods with average ambient temperatures of 10–15 and 26–32 1C, respectively, with the instrument operating at ambient outdoor temperatures. Analysis of the chemical composition of the particles sampled in these studies shows that sampling conditions can strongly impact the determination of suitable markers for identifying particles emitted from different vehicle types during ambient studies

The EDAM Project: Mining Atmospheric Aerosol Datasets

Data mining has been a very active area of research in the database, machine learning, and mathematical programming communities in recent years. EDAM (Exploratory Data Analysis and Management) is a joint project between researchers in Atmospheric Chemistry and Computer Science at Carleton College and the University of Wisconsin-Madison that aims to develop data mining techniques for advancing the state of the art in analyzing atmospheric aerosol datasets. There is a great need to better understand the sources, dynamics, and compositions of atmospheric aerosols. The traditional approach for particle measurement, which is the collection of bulk samples of particulates on filters, is not adequate for studying particle dynamics and real-time correlations. This has led to the development of a new generation of real-time instruments that provide continuous or semi-continuous streams of data about certain aerosol properties. However, these instruments have added a significant level of complexity to atmospheric aerosol data, and dramatically increased the amounts of data to be collected, managed, and analyzed. Our abilit y to integrate the data from all of these new and complex instruments now lags far behind our data-collection capabilities, and severely limits our ability to understand the data and act upon it in a timely manner. In this paper, we present an overview of the EDAM project. The goal of the project, which is in its early stages, is to develop novel data mining algorithms and approaches to managing and monitoring multiple complex data streams. An important objective is data quality assurance, and real-time data mining offers great potential. The approach that we take should also provide good techniques to deal with gas-phase and semi-volatile data. While atmospheric aerosol analysis is an important and challenging domain that motivates us with real problems and serves as a concrete test of our results, our objective is to develop techniques that have broader applicability, and to explore some fundamental challenges in data mining that are not specific to any given application domain

Emissions from Ethanol-Gasoline Blends: A Single Particle Perspective

Due to its agricultural origin and function as a fuel oxygenate, ethanol is being promoted as an alternative biomass-based fuel for use in spark ignition engines, with mandates for its use at state and regional levels. While it has been established that the addition of ethanol to a fuel reduces the particulate mass concentration in the exhaust, little attention has been paid to changes in the physicochemical properties of the emitted particles. In this work, a dynamometer-mounted GM Quad-4 spark ignition engine run without aftertreatment at 1,500 RPM and 100% load was used with four different fuel blends, containing 0, 20, 40 and 85 percent ethanol in gasoline. This allowed the effects of the fuel composition to be isolated from other effects. Instrumentation employed included two Aerosol Time-of-Flight Mass Spectrometers covering different size ranges for analysis of single particle composition, an Aethalometer for black carbon, a Scanning Mobility Particle Sizer for particle size distributions, a Photoelectric Aerosol Sensor for particle-bound polycyclic aromatic hydrocarbon (PAH) species and gravimetric filter measurements for particulate mass concentrations. It was found that, under the conditions investigated here, additional ethanol content in the fuel changes the particle size distribution, especially in the accumulation mode, and decreases the black carbon and total particulate mass concentrations. The molecular weight distribution of the PAHs was found to decrease with added ethanol. However, PAHs produced from higher ethanol-content fuels are associated with NO2 − (m/z—46) in the single-particle mass spectra, indicating the presence of nitro-PAHs. Compounds associated with the gasoline (e.g., sulfur-containing species) are diminished due to dilution as ethanol is added to the fuel relative to those associated with the lubricating oil (e.g., calcium, zinc, phosphate) in the single particle spectra. These changes have potential implications for the health effect impacts of particulate emissions from biofuel blends

Estimating the Contribution of Point Sources to Atmospheric Metals Using Single-particle Mass Spectrometry

Single-particle mass spectra were collected using an Aerosol Time-of-Flight Mass Spectrometer (ATOFMS) during December of 2003 and February of 2004 at an industrially impacted location in East St. Louis, IL. Hourly integrated peak areas for twenty ions were evaluated for their suitability in representing metals/metalloids, particularly those reported in the US EPA Toxic Release Inventory (TRI). Of the initial twenty ions examined, six (Al, As, Cu, Hg, Ti, and V) were found to be unsuitable due to strong isobaric interferences with commonly observed organic fragments, and one (Be) was found to have no significant signal. The usability of three ions (Co, Cr, and Mn) was limited due to suspected isobaric interferences based on temporal comparisons with commonly observed organic fragments. The identity of the remaining ions (Sb, Ba, Cd, Ca, Fe, Ni, Pb, K, Se, and Zn) was substantiated by comparing their signals with the integrated hourly signals of one or more isotope ions. When compared with one-in-six day integrated elemental data as determined by X-ray fluorescence spectroscopy (XRF), the daily integrated ATOFMS signal for several metal ions revealed a semi-quantitative relationship between ATOFMS peak area and XRF concentrations, although in some cases comparison of these measurements were poor at low elemental concentrations/ion signals due to isobaric interferences. A method of estimating the impact of local point sources was developed using hourly integrated ATOFMS peak areas, and this method attributed as much as 85% of the concentration of individual metals observed at the study site to local point sources. Hourly surface wind data were used in conjunction with TRI facility emissions data to reveal likely point sources impacting metal concentrations at the study site and to illustrate the utility of using single-particle mass spectral data to characterize atmospheric metals and identify point sources

Subarctic Atmospheric Aerosol Composition: 2. Hygroscopic Growth Properties

Subarctic aerosols were sampled during July 2007 at the Abisko Scientific Research Station Stordalen site in northern Sweden with an instrument setup consisting of a custombuilt Hygroscopicity Tandem Differential Mobility Analyzer (HTDMA) connected in series to a single particle mass spectrometer. Aerosol chemical composition in the form of bipolar single particle mass spectra was determined as a function of hygroscopic growth both in situ and in real time. The HTDMA was deployed at a relative humidity of 82%, and particles with a dry mobility diameter of 260 nm were selected. Aerosols from two distinct air masses were analyzed during the sampling period. Sea salt aerosols were found to be the dominant particle group with the highest hygroscopicity. High intensities of sodium and related peaks in the mass spectra were identified as exclusive markers for large hygroscopic growth. Particles from biomass combustion were found to be the least hygroscopic aerosol category. Species normally considered soluble (e.g., sulfates and nitrates) were found in particles ranging from high to low hygroscopicity. Furthermore, the signal intensities of the peaks related to these species did not correlate with hygroscopicity

Randomized Trial of Time-Limited Interruptions of Protease Inhibitor-Based Antiretroviral Therapy (ART) vs. Continuous Therapy for HIV-1 Infection

Background The clinical outcomes of short interruptions of PI-based ART regimens remains undefined. Methods A 2-arm non-inferiority trial was conducted on 53 HIV-1 infected South African participants with viral load/ml and CD4 T cell count \u3e450 cells/µl on stavudine (or zidovudine), lamivudine and lopinavir/ritonavir. Subjects were randomized to a) sequential 2, 4 and 8-week ART interruptions or b) continuous ART (cART). Primary analysis was based on the proportion of CD4 count \u3e350 cells(c)/ml over 72 weeks. Adherence, HIV-1 drug resistance, and CD4 count rise over time were analyzed as secondary endpoints. Results The proportions of CD4 counts \u3e350 cells/µl were 82.12% for the intermittent arm and 93.73 for the cART arm; the difference of 11.95% was above the defined 10% threshold for non-inferiority (upper limit of 97.5% CI, 24.1%; 2-sided CI: −0.16, 23.1). No clinically significant differences in opportunistic infections, adverse events, adherence or viral resistance were noted; after randomization, long-term CD4 rise was observed only in the cART arm. Conclusion We are unable to conclude that short PI-based ART interruptions are non-inferior to cART in retention of immune reconstitution; however, short interruptions did not lead to a greater rate of resistance mutations or adverse events than cART suggesting that this regimen may be more forgiving than NNRTIs if interruptions in therapy occur

Closed-loop Stimulation of Temporal Cortex Rescues Functional Networks and Improves Memory

Memory failures are frustrating and often the result of ineffective encoding. One approach to improving memory outcomes is through direct modulation of brain activity with electrical stimulation. Previous efforts, however, have reported inconsistent effects when using open-loop stimulation and often target the hippocampus and medial temporal lobes. Here we use a closed-loop system to monitor and decode neural activity from direct brain recordings in humans. We apply targeted stimulation to lateral temporal cortex and report that this stimulation rescues periods of poor memory encoding. This system also improves later recall, revealing that the lateral temporal cortex is a reliable target for memory enhancement. Taken together, our results suggest that such systems may provide a therapeutic approach for treating memory dysfunction