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.