418 research outputs found

    Size-resolved particulate matter composition in Beijing during pollution and dust events

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    Each spring, Beijing, China, experiences dust storms which cause high particulate matter concentrations. Beijing also has many anthropogenic sources of particulate matter including the large Capitol Steel Company. On the basis of measured size segregated, speciated particulate matter concentrations, and calculated back trajectories, three types of pollution events occurred in Beijing from 22 March to 1 April 2001: dust storms, urban pollution events, and an industrial pollution event. For each event type, the source of each measured element is determined to be soil or anthropogenic and profiles are created that characterize the particulate matter composition. Dust storms are associated with winds traveling from desert regions and high total suspended particle (TSP) and PM2.5 concentrations. Sixty-two percent of TSP is due to elements with oxides and 98% of that is from soil. Urban pollution events have smaller particulate concentrations but 49% of the TSP is from soil, indicating that dust is a major component of the particulate matter even when there is not an active dust storm. The industrial pollution event is characterized by winds from the southwest, the location of the Capitol Steel Company, and high particulate concentrations. PM2.5 mass and acidic ion concentrations are highest during the industrial pollution event as are Mn, Zn, As, Rb, Cd, Cs and Pb concentrations. These elements can be used as tracers for industrial pollution from the steel mill complex. The industrial pollution is potentially more detrimental to human health than dust storms due to higher PM2.5 concentrations and higher acidic ion and toxic particulate matter concentrations

    Seasonal trends in PM2.5 source contributions in Beijing, China

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    The 24-h PM2.5 samples (particles with an aerodynamic diameter of 2.5 μm or less) were taken at 6-day intervals at five urban and rural sites simultaneously in Beijing, China for 1 month in each quarter of calendar year 2000. Samples at each site were combined into a monthly composite for the organic tracer analysis by GC/MS (gas chromatography/mass spectrometry). Compared to the data obtained from other metropolitan cities in the US, the PM2.5 mass and fine organic carbon (OC) concentrations in Beijing were much higher with an annual average of 101 and 20.9 μg m^(−3), respectively. Over one hundred organic compounds including unique tracers for important sources were quantified in PM2.5 in Beijing. Source apportionment of fine OC was conducted using chemical mass balance receptor model (CMB) in combination with particle-phase organic compounds as fitting tracers. Carbonaceous aerosols and major ions (sulfate, nitrate and ammonium) constituted 69% of PM2.5 mass on average. The major sources of PM2.5 mass in Beijing averaged over five sites on an annual basis were determined as dust (20%), secondary sulfate (17%), secondary nitrate (10%), coal combustion (7%), diesel and gasoline exhaust (7%), secondary ammonium (6%), biomass aerosol (6%), cigarette smoke (1%), and vegetative detritus (1%). The lowest PM2.5 mass concentration was found in January (60.9 μg m^(−3)), but the contribution of carbonaceous aerosol to PM2.5 mass was maximal during this season, accounting for 57% of the mass. During cold heating season, the contributions from coal combustion and biomass aerosol to PM2.5 mass increased, accounting for 20.9% of fine particle mass in October and 24.5% in January. The contribution of the biomass aerosols peaked in the fall. In April 2000, the impact of dust storms was so significant that dust alone constituted 36% of PM2.5 mass. On average, the model resolved 88% of the sources of the PM2.5 mass concentrations in Beijing

    The EDAM Project: Mining Atmospheric Aerosol Datasets

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    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
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