Primary and secondary carbonaceous species in PM2.5 samples in Milan (Italy)

Seasonal elemental carbon (EC) and organic carbon (OC) concentration levels in PM2.5 samples collected in Milan (Italy) are presented and discussed, enriching the world-wide database of carbonaceous species in fine particulate matter (PM). High-volume PM2.5 sampling campaigns were performed from August 2002 through December 2003 in downtown Milan at an urban background site. Compared to worldwide average concentrations, in Milan warm-season OC and both warm- and cold-season EC are relatively low; conversely, cold-season OC concentrations are rather high. Consequently, high values for the OC/EC ratio are observed, especially in the winter period. The relation between OC/EC ratio values and wind direction is investigated, pointing out that the highest ratios are associated to winds blowing from those nearby areas where wood consumption for domestic heating is larger. Information on the OC partitioning between its primary and secondary fraction are derived by means of the EC-tracer method and principal component analysis. In the warm-season, OC is mainly of secondary origin, secondary organic aerosol (SOA) accounting for about 84% of the particulate organic matter and 25–28% of the PM2.5 mass. For the cold season the full application of the EC-tracer method was not possible and the primary organic aerosol deriving from traffic could only be estimated. However, principal component analysis (PCA) suggest a prevailing primary origin for OC, thus raising the attention on space heating emissions, and on wood combustion in particular, for air quality control. The role of traffic emissions on PM2.5 concentration levels, as a primary source, are also assessed: EC and primary organic matter from traffic account for a warm-season 30% and a cold-season 7% of the total carbon in PM2.5, that is for about 10% and 6% of PM2.5 mass, respectively. This latter small primary contribution estimated for the cold-season points out that stationary sources, which were not thought to play a significant role on PM concentration levels, may conversely be as much responsible for ambient particulate pollution.     

南宁市大气颗粒物TSP、PM10、PM2.5污染水平研究

2002年在南宁市的5个典型城市功能区内,共采集了125个大气样品(按季节分别采集),初步调查了大气中颗粒物TSP、PM10、PM2.5的污染状况。结果表明,南宁市TSP、PM10、PM2.5的污染很严重,超标率分别为67．5％、82．5％、92．5％,对人体健康危害更大的PM2．5占到了PM10的63．5％左右。重污染区PM2.5的浓度超过轻污染区近一倍。     

Elemental characterization of PM10, PM2.5 and PM1 in the town of Genoa (Italy)

The particulate matter (PM) concentration and composition, the PM10, PM2.5, PM1 fractions, were studied in the urban area of Genoa, a coastal town in the northwest of Italy. Two instruments, the continuous monitor TEOM and the sequential sampler PARTISOL, were operated almost continuously on the same site from July 2001 to September 2004. Samples collected by PARTISOL were weighted to obtain PM concentration and then analysed by PIXE (particle induced X-ray emission) and by ED-XRF (energy dispersion X-ray fluorescence), obtaining concentrations for elements from Na to Pb. Some of the filters used in the TEOM microbalance were analysed by ED-XRF to calculate Pb concentration values averaged over 7-30 d periods.     

Oxalic acid in PM2.5 particulate matter in California

Ambient air monitoring for organic acids in PM2.5 was conducted at several locations in California. During the study, it was found that oxalic acid (ethanedioc acid) was the most abundant organic acid found in the PM2.5 fraction. Samples from Azuza (in southern California), San Jose (in the San Francisco Bay area), and Fresno (in central California), a PM2.5 Super Site, were collected in 1999 and analyzed. The results for oxalic acid concentrations during this monitoring effort are presented.     

南昌市大气PM2.5中多环芳烃的来源解析

在南昌市布设5个采样点,分别代表工业区、居住区、交通干线区、商业区以及郊区,于2007年7～8月进行大气PM2.5的采样.根据5个采样点测得的数据,通过因子分析法判断南昌市大气PM2.5中多环芳烃的主要来源,再利用多元线性回归法确定各主要来源对多环芳烃的贡献率.结果表明,南昌市多环芳烃的主要来源为车辆排放源、高温加热源、燃煤污染源,对多环芳烃的贡献率分别为37.9%、28.2%、22.0%.     

Evaluation of artificial neural networks for fine particulate pollution (PM10 and PM2.5) forecasting

Multi-layer perceptron (MLP) artificial neural network (ANN) models are compared with traditional multiple regression (MLR) models for daily maximum and average O3 and particulate matter (PM10 and PM2.5) forecasting. MLP particulate forecasting models show little if any improvement over MLR models and exhibit less skill than do O3 forecasting models. Meteorological variables (precipitation, wind, and temperature), persistence, and co-pollutant data are shown to be useful PM predictors. If MLP approaches are adopted for PM forecasting, training methods that improve extreme value prediction are recommended.     

Correlations between fine particulate matter (PM2.5) and meteorological variables in the United States: Implications for the sensitivity of PM2.5 to climate change

We applied a multiple linear regression (MLR) model to study the correlations of total PM_2.5 and its components with meteorological variables using an 11-year (1998–2008) observational record over the contiguous US. The data were deseasonalized and detrended to focus on synoptic-scale correlations. We find that daily variation in meteorology as described by the MLR can explain up to 50% of PM_2.5 variability with temperature, relative humidity (RH), precipitation, and circulation all being important predictors. Temperature is positively correlated with sulfate, organic carbon (OC) and elemental carbon (EC) almost everywhere. The correlation of nitrate with temperature is negative in the Southeast but positive in California and the Great Plains. RH is positively correlated with sulfate and nitrate, but negatively with OC and EC. Precipitation is strongly negatively correlated with all PM_2.5 components. We find that PM_2.5 concentrations are on average 2.6 μg m^?3 higher on stagnant vs. non-stagnant days. Our observed correlations provide a test for chemical transport models used to simulate the sensitivity of PM_2.5 to climate change. They point to the importance of adequately representing the temperature dependence of agricultural, biogenic and wildfire emissions in these models.     

Characterization of soluble and insoluble components in PM2.5 and PM10 fractions of airborne particulate matter in Kofu city,Japan

A systematic method combining water and diluted-acid extractions has been developed for the manifold evaluation of soluble and insoluble fractions in ambient aerosol. The pre-washed regenerated cellulose membrane filter was used as a collection medium of a low-volume air sampler. The collection time of 7–14 days was required to obtain the sample amounts enough for the systematic analysis. Simple and efficient extraction procedures using the filtration of water and 0.1 M hydrochloric acid were recommended in order to obtain the information about the dissolution behaviors of various elements in the aerosol. Soluble components in both the extracts were determined by inductively coupled plasma atomic emission spectrometry (ICP-AES) and ion chromatography (IC). These extraction procedures were also preferred to prepare thin-layer specimens suitable to the succeeding X-ray fluorescence spectrometry (XRF) for insoluble components. Elemental compositions of the extraction residues were conveniently determined by the XRF calibrated with thin-layer standard specimens prepared with activated carbon. The determination of the 17 representative elements (Na, Mg, Al, Si, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Cu, Zn, Br, Pb) in these three fractions from an aerosol sample was performed rapidly within 4 h. The proposed systematic method was applied to PM_2.5 and PM₁₀ aerosol samples collected in Kofu City, Central Japan, and the enrichment behaviors of various elements and their source apportionment such as soil, anthropogenic substances and vehicle exhaust particulates could be demonstrated by the present method.     

Monitoring of PM10 and PM2.5 around primary particulate anthropogenic emission sources

Investigations on the monitoring of ambient air levels of atmospheric particulates were developed around a large source of primary anthropogenic particulate emissions: the industrial ceramic area in the province of Castelló (Eastern Spain). Although these primary particulate emissions have a coarse grain-size distribution, the atmospheric transport dominated by the breeze circulation accounts for a grain-size segregation, which results in ambient air particles occurring mainly in the 2.5–10 μm range. The chemical composition of the ceramic particulate emissions is very similar to the crustal end-member but the use of high Al, Ti and Fe as tracer elements as well as a peculiar grain-size distribution in the insoluble major phases allow us to identify the ceramic input in the bulk particulate matter. PM2.5 instead of PM10 monitoring may avoid the interference of crustal particles without a major reduction in the secondary anthropogenic load, with the exception of nitrate. However, a methodology based in PM2.5 measurement alone is not adequate for monitoring the impact of primary particulate emissions (such as ceramic emissions) on air quality, since the major ambient air particles derived from these emissions are mainly in the range of 2.5–10 μm. Consequently, in areas characterised by major secondary particulate emissions, PM2.5 monitoring should detect anthropogenic particulate pollutants without crustal particulate interference, whereas PM10 measurements should be used in areas with major primary anthropogenic particulate emissions.     

Particulate matter in California: part 1--Intercomparison of several PM2.5, PM10-2.5, and PM10 monitoring networks

It will be many years before the recently deployed network of fine particulate matter with an aerodynamic diameter less than 2.5 microm (PM2.5) Federal Reference Method (FRM) samplers produces information on nonattainment areas, trends, and source impacts. However, data on PM2.5 and its major constituents have been routinely collected in California for the past 20 years. The California Air Resources Board operated as many as 20 dichotomous (dichot) samplers for PM2.5 and coarse PM (PM10-2.5). The California Acid Deposition Monitoring Program (CADMP) collected 12-h-average PM2.5 and PM10 from 1988 to 1995 at ten urban and rural sites and 24-h-average PM2.5 at five urban sites since 1995. Beginning in 1994, the Children's Health Study collected 2-week averages of PM2.5 in 12 communities in southern California using the Two-Week Sampler (TWS). Comparisons of collocated samples establish relationships between the dichot, CADMP, and TWS samplers and the 82-site network of PM2.5 FRM samplers deployed since 1999 in California. PM mass data from the different monitoring programs have modest to high correlation to FRM mass data, fairly small systematic biases and negative proportional biases ranging from 7 to 22%. If the biases are taken into account, all of the programs should be considered comparable with the FRM program. Thus, historical data can be used to develop long-term PM trends in California.     

Determinants of exposure to fine particulate matter (PM2.5) for waiting passengers at bus stops

This research evaluates commuter exposure to particulate matter during pre-journey commute segments for passengers waiting at bus stops by investigating 840 min of simultaneous exposure levels, both inside and outside seven bus shelters in Buffalo, New York. A multivariate regression model is used to estimate the relation between exposure to particulate matter (PM_2.5 measured in μg m^?3) and three vectors of determinants: time and location, physical setting and placement, and environmental factors. Four determinants have a statistically significant effect on particulate matter: time of day, passengers’ waiting location, land use near the bus shelter, and the presence of cigarette smoking at the bus shelter. Model results suggest that exposure to PM_2.5 inside a bus shelter is 2.63 μg m^?3 (or 18 percent) higher than exposure outside a bus shelter, perhaps due in part to the presence of cigarette smoking. Morning exposure levels are 6.51 μg m^?3 (or 52 percent) higher than afternoon levels. Placement of bus stops can affect exposure to particulate matter for those waiting inside and outside of shelters: air samples at bus shelters located in building canyons have higher particulate matter than bus shelters located near open space.     

Source apportionment of fine particulate matter (PM2.5) at a rural Ohio River Valley site

Twenty four-hour averaged concentrations of fine particulate matter were collected at Athens, OH between March 2004 and November 2005 in an effort to characterize the nature of PM_2.5 and apportion its sources. PM_2.5 samples were chemically analyzed and positive matrix factorization was applied to this speciation data to identify the probable sources. PMF arrived at a 7-factor model to most accurately apportion sources of the PM_2.5 observed at Athens. Conditional probability function (CPF) and potential source contribution function (PSCF) were applied to the identified sources to investigate the geographical location of these sources. Secondary sulfate source dominated the contributions with a total contribution of 62.6% with the primary and secondary organic source following second with 19.9%. Secondary nitrate contributed a total of 6.5% with the steel production source and Pb- and Zn-source coming in at 3.1% and 2.9%, respectively. Crustal and mobile sources were small contributors (2.5% each) of PM_2.5 to the Athens region. The secondary sulfate, secondary organic and nitrate portrayed a clear seasonal nature with the sulfate and secondary organic peaking in the warm months and the nitrate reaching a high in the cold months. The high percentage of secondary sulfate observed at a rural site like Athens suggests the involvement of regional transport mechanisms.     

光散射颗粒物监测仪在环境空气监测中的适用性

为了研究光散射颗粒物监测仪在环境空气监测中的适应性,参照《环境空气颗粒物(PM10和PM2.5)连续自动检测技术要求及检测方法》(HJ 653-2013),在北京秋季使用PQ200(滤膜采样器)对DustTrak8530、LD-6S、HBKLW-2共3种光散射仪器进行比对测试.结果表明,3种光散射仪器的平行性都达标;在监测PM10时,3种仪器与PQ200的线性相关系数都达标,斜率只有HBKLW-2达标,截距除HBKLW-2略微超标外都与标准相差较远;在监测PM2.5时,3种仪器与PQ200的线性相关系数都达标且优于PM10,斜率只有HBKLW-2和LD-6S达标,截距绝对值相比PM10有所减小,但只有HBKLW-2达标;经校正因子修正后,3种仪器与PQ200的线性回归斜率达标、相关系数不变、监测PM2.5的截距相比PM10更加接近标准值,故光散射仪器更加适用于环境空气PM2.5监测.     

Nonlinear analysis and prediction of coarse particulate matter concentration in ambient air

This study attempts to characterize and predict coarse particulate matter (PM10) concentration in ambient air using the concepts of nonlinear dynamical theory. PM10 data observed daily from 1999 to 2002 at a site in Mumbai, India, was used to study the applicability of the chaos theory. First, the autocorrelation function and Fourier power spectrum were used to analyze the behavior of the time-series. The dynamics of the time-series was additionally studied through correlation integral analysis and phase space reconstruction. The nonlinear predictions were then obtained using local polynomial approximation based on the reconstructed phase space. The results were then compared with the autoregressive model. The results of nonlinear analysis indicated the presence of chaotic character in the PM10 time-series. It was also observed that the nonlinear local approximation outperforms the autoregressive model, because the observed relative error of prediction for the autoregressive model was greater than the local approximation model. The invariant measures of nonlinear dynamics computed for the predicted time-series using the two models also supported the same findings.     

宁波市大气可吸入颗粒物PM1o和PM2.5的源解析研究

在宁波市布设4个代表性点位,于2010年春季、夏季和冬季进行大气PM10和PM2.s的采样,同时采集了多种颗粒物源样品,建立了PM10、PM2.5和源样品的化学成分谱.采用化学质量平衡模型(CMB)对宁波市PM10、PM2.5进行源解析.结果表明,城市扬尘、煤烟尘、机动车尾气尘是宁波市PM10、PM2.5的3大污染源,...     

A review of the impact of fireworks on particulate matter in ambient air

To determine the impact of fireworks (FW) and firecrackers (FC) on particulate matter (PM) in ambient air, we reviewed evidence related to ambient PM during FW/FC periods; specifically, PM concentration, size, morphology, chemical components, including water-soluble ions and trace metals, and associated human health risks caused by exposure to FW/FC PM were reviewed. A large body of research suggests that outdoor ambient PM levels increase significantly during FW/FC displays. Furthermore, FW/FC PM remains suspended in the air, contributing to high PM concentrations for a long period. Increased PM from burning FW and FC mainly comprises fine and ultrafine spherical particles. Elevated levels of various trace metals, ions, elemental carbon (EC), organic carbon (OC), and organics in PM are present during FW/FC periods.Implications: Unique physical and chemical properties of ambient PM during short-term FW/FC burning can lead to a substantial increase in adverse health effects compared with during non-FW/FC periods. Further epidemiological and toxicological research into the potential health effects resulting from exposure to various pollutants from FW/FC is vital. Geographical distributions of PM concentrations during FW displays highlight the importance of implementing PM controls at the regional level and formulating stricter protective environmental legislation, particularly in Asian (e.g., India, China, or Taiwan) where festivals are not the only periods celebrated with FW/FC.     

Comparison and evaluation of chemically speciated mobile source PM2.5 particulate matter profiles

Mobile sources are significant contributors to ambient PM2.5, accounting for 50% or more of the total observed levels in some locations. One of the important methods for resolving the mobile source contribution is through chemical mass balance (CMB) receptor modeling. CMB requires chemically speciated source profiles with known uncertainty to ensure accurate source contribution estimates. Mobile source PM profiles are available from various sources and are generally in the form of weight fraction by chemical species. The weight fraction format is commonly used, since it is required for input into the CMB receptor model. This paper examines the similarities and differences in mobile source PM2.5 profiles that contain data for elements, ions, elemental carbon (EC) and organic carbon (OC), and in some cases speciated organics (e.g., polycyclic aromatic hydrocarbons [PAHs]), drawn from four different sources. Notable characteristics of the mass fraction data include variability (relative contributions of elements and ions) among supposedly similar sources and a wide range of average EC:OC ratios (0.60 +/- 0.53 to 1.42 +/- 2.99) for light-duty gasoline vehicles (LDGVs), indicating significant EC emissions from LDGVs in some cases. For diesel vehicles, average EC:OC ratios range from 1.09 +/- 2.66 to 3.54 +/- 3.07. That different populations of the same class of emitters can show considerable variability suggests caution should be exercised when selecting and using profiles in source apportionment studies.     

Source apportionment of particulate matter (PM2.5) in an urban area using dispersion,receptor and inverse modelling

Air pollution emission inventories are the basis for air quality assessment and management strategies. The quality of the inventories is of great importance since these data are essential for air pollution impact assessments using dispersion models. In this study, the quality of the emission inventory for fine particulates (PM_2.5) is assessed: first, using the calculated source contributions from a receptor model; second, using source apportionment from a dispersion model; and third, by applying a simple inverse modelling technique which utilises multiple linear regression of the dispersion model source contributions together with the observed PM_2.5 concentrations. For the receptor modelling the chemical composition of PM_2.5 filter samples from a measurement campaign performed between January 2004 and April 2005 are analysed. Positive matrix factorisation is applied as the receptor model to detect and quantify the various source contributions. For the same observational period and site, dispersion model calculations using the Air Quality Management system, AirQUIS, are performed. The results identify significant differences between the dispersion and receptor model source apportionment, particularly for wood burning and traffic induced suspension. For wood burning the receptor model calculations are lower, by a factor of 0.54, but for the traffic induced suspension they are higher, by a factor of 7.1. Inverse modelling, based on regression of the dispersion model source contributions and the PM_2.5 concentrations, indicates similar discrepancies in the emissions inventory. In order to assess if the differences found at the one site are generally applicable throughout Oslo, the individual source category emissions are rescaled according to the receptor modelling results. These adjusted PM_2.5 concentrations are compared with measurements at four independent stations to evaluate the updated inventory. Statistical analysis shows improvement in the estimated concentrations for PM_2.5 at all sites. Similarly, inverse modelling is applied at these independent sites and this confirms the validity of the receptor model results.     

Uncertainty associated with the gravimetric measurement of particulate matter concentration in ambient air

This work applied a propagation of uncertainty method to typical total suspended particulate (TSP) sampling apparatus in order to estimate the overall measurement uncertainty. The objectives of this study were to estimate the uncertainty for three TSP samplers, develop an uncertainty budget, and determine the sensitivity of the total uncertainty to environmental parameters. The samplers evaluated were the TAMU High Volume TSP Sampler at a nominal volumetric flow rate of 1.42 m³ min^–1 (50 CFM), the TAMU Low Volume TSP Sampler at a nominal volumetric flow rate of 17 L min^–1 (0.6 CFM) and the EPA TSP Sampler at the nominal volumetric flow rates of 1.1 and 1.7 m³ min^–1 (39 and 60 CFM). Under nominal operating conditions the overall measurement uncertainty was found to vary from 6.1 x 10^–6 g m^–3 to 18.0 x 10^–6 g m^–3, which represented an uncertainty of 1.7% to 5.2% of the measurement. Analysis of the uncertainty budget determined that three of the instrument parameters contributed significantly to the overall uncertainty: the uncertainty in the pressure drop measurement across the orifice meter during both calibration and testing and the uncertainty of the airflow standard used during calibration of the orifice meter. Five environmental parameters occurring during field measurements were considered for their effect on overall uncertainty: ambient TSP concentration, volumetric airflow rate, ambient temperature, ambient pressure, and ambient relative humidity. Of these, only ambient TSP concentration and volumetric airflow rate were found to have a strong effect on the overall uncertainty. The technique described in this paper can be applied to other measurement systems and is especially useful where there are no methods available to generate these values empirically.

Implications:?This work addresses measurement uncertainty of TSP samplers used in ambient conditions. Estimation of uncertainty in gravimetric measurements is of particular interest, since as ambient particulate matter (PM) concentrations approach regulatory limits, the uncertainty of the measurement is essential in determining the sample size and the probability of type II errors in hypothesis testing. This is an important factor in determining if ambient PM concentrations exceed regulatory limits. The technique described in this paper can be applied to other measurement systems and is especially useful where there are no methods available to generate these values empirically.