The existence and importance of peroxyformic acid (PFA) in the atmosphere has been under controversy. We present here, for the first time, the observation data for PFA from four field measurements carried out in China. These data provided powerful evidence that PFA can stay in the atmosphere, typically in dozens of pptv level. The relationship between PFA and other detected peroxides was examined. The results showed that PFA had a strong positive correlation with its homolog, peroxyacetic acid, due to their similar sources and sinks. Through an evaluation of PFA production and removal rates, we proposed that the reactions between peroxyformyl radical (HC(O)O2) and formaldehyde or the hydroperoxyl radical (HO2) were likely to be the major source and degradation into formic acid (FA) was likely to be the major sink for PFA. Based on a box model evaluation, we proposed that the HC(O)O2 and PFA chemistry was a major source for FA under low NOx conditions. Furthermore, it is found that the impact of the HC(O)O2 and PFA chemistry on radical cycling was dependent on the yield of HC(O)O2 radical from HC(O) + O2 reaction. When this yield exceeded 50%, the HC(O)O2 and PFA chemistry should not be neglected for calculating the radical budget. To make clear the exact importance of HC(O)O2 and PFA chemistry in the atmosphere, further kinetic, field and modeling studies are required.
The objective of this study was to remove systematic bias among fine particulate matter (PM2.5) mass concentration measurements made by different types of samplers used in the Pittsburgh Aerosol Research and Inhalation Epidemiology Study (PARIES). PARIES is a retrospective epidemiology study that aims to provide a comprehensive analysis of the associations between air quality and human health effects in the Pittsburgh, Pennsylvania, region from 1999 to 2008. Calibration was needed in order to minimize the amount of systematic error in PM2.5 exposure estimation as a result of including data from 97 different PM2.5 samplers at 47 monitoring sites. Ordinary regression often has been used for calibrating air quality measurements from pairs of measurement devices; however, this is only appropriate when one of the two devices (the "independent" variable) is free from random error, which is rarely the case. A group of methods known as "errors-in-variables" (e.g., Deming regression, reduced major axis regression) has been developed to handle calibration between two devices when both are subject to random error, but these methods require information on the relative sizes of the random errors for each device, which typically cannot be obtained from the observed data. When data from more than two devices (or repeats of the same device) are available, the additional information is not used to inform the calibration. A more general approach that often has been overlooked is the use of a measurement error structural equation model (SEM) that allows the simultaneous comparison of three or more devices (or repeats). The theoretical underpinnings of all of these approaches to calibration are described, and the pros and cons of each are discussed. In particular, it is shown that both ordinary regression (when used for calibration) and Deming regression are particular examples of SEMs but with substantial deficiencies. To illustrate the use of SEMs, the 7865 daily average PM2.5 mass concentration measurements made by seven collocated samplers at an urban monitoring site in Pittsburgh, Pennsylvania, were used. These samplers, which included three federal reference method (FRM) samplers, three speciation samplers, and a tapered element oscillating microbalance (TEOM), operated at various times during the 10-year PARIES study period. Because TEOM measurements are known to depend on temperature, the constructed SEM provided calibration equations relating the TEOM to the FRM and speciation samplers as a function of ambient temperature. It was shown that TEOM imprecision and TEOM bias (relative to the FRM) both decreased as temperature increased. It also was shown that the temperature dependency for bias was non-linear and followed a sigmoidal (logistic) pattern. The speciation samplers exhibited only small bias relative to the FRM samplers, although the FRM samplers were shown to be substantially more precise than both the TEOM and the speciation samplers. Comparison of the SEM results to pairwise simple linear regression results showed that the regression results can differ substantially from the correctly-derived calibration equations, especially if the less-precise device is used as the independent variable in the regression. (C) 2014 Elsevier Ltd. All rights reserved.
Airborne particles in urban Beijing during haze days and normal days were collected and analyzed in the autumn and winter seasons to reveal the chemical characteristics variations of air pollution. The air quality in haze days was substantially worse than that in normal days. Both the relatively low wind speed and high relative humidity were in favor of the accumulation of pollution species and new formation of secondary PM2.5 in the atmosphere. Elevated concentrations of elements and water-soluble inorganic ions were found on haze days for both PM10 and PM2.5. Particularly, the crustal element, such as Fe, in both PM10 and PM2.5 were substantially higher in autumn normal days and winter haze days than those in autumn haze days and winter normal days, indicating that the abundance of Fe in autumn haze days mainly be originated from crustal dust while in winter haze days it might be primarily emitted from anthropogenic sources (iron and steel smelting) instead of road dust. Secondary ion species (SO42−, NO3−, NH4+) in particles were generated much more during haze episodes, and contributed a higher proportion in PM2.5 than in PM10 during the two sampling periods. Moreover, HYSPLIT model was used to explain the possible transport of airborne particles from distant sources. By comparing with south-type trajectory, west-type trajectory entrained larger amounts of primary crustal pollutants, while, south-type trajectory was comprised of a higher mass of anthropogenic pollution species. The results of back trajectory analysis indicated that the elevated concentration of aerosol and its chemical components during haze days might be caused by the integrated effects of accumulation under stagnant meteorological condition and the transport emissions of pollutants from anthropogenic sources surrounding Beijing city.
Towards low bit rate mobile visual search, recent works have proposed to aggregate the local features and compress the aggregated descriptor (such as Fisher vector, the vector of locally aggregated descriptors) for low latency query delivery as well as moderate search complexity. Even though Hamming distance can be computed very fast, the computational cost of exhaustive linear search over the binary descriptors grows linearly with either the length of a binary descriptor or the number of database images. In this paper, we propose a novel weighted component hashing (WeCoHash) algorithm for long binary aggregated descriptors to significantly improve search efficiency over a large scale image database. Accordingly, the proposed WeCoHash has attempted to address two essential issues in Hashing algorithms: "what to hash" and "how to search." "What to hash" is tackled by a hybrid approach, which utilizes both image-specific component (i.e., visual word) redundancy and bit dependency within each component of a binary aggregated descriptor to produce discriminative hash values for bucketing. "How to search" is tackled by an adaptive relevance weighting based on the statistics of hash values. Extensive comparison results have shown that WeCoHash is at least 20 times faster than linear search and 10 times faster than local sensitive hash (LSH) when maintaining comparable search accuracy. In particular, the WeCoHash solution has been adopted by the emerging MPEG compact descriptor for visual search (CDVS) standard to significantly speed up the exhaustive search of the binary aggregated descriptors.