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.     

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%.     

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.     

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.     

Determinants of fine particle (PM2.5) personal exposure levels in transport microenvironments,London, UK

A series of field studies were carried out in London, UK, during 1999–2000 in which over 400 fine particle (PM_2.5) personal exposure level measurements were taken for journeys in bicycle, bus, car and underground rail transport microenvironments. This was the first comprehensive PM_2.5 personal exposure study of transport users. Both a fixed-route multi-transport mode study and a study of cyclists’ commuter journeys were undertaken. Subsequent to these field studies regression modelling of possible influencing factors of these exposure levels was carried out. Meteorological variables, traffic density, mode and route were considered; the relationships of personal exposure levels with fixed site monitor (FSM) concentrations, and of the FSM concentrations with the potential predictor variables, were also investigated. This analysis of the determinants of transport user exposure to PM_2.5 in London, UK, showed that wind speed had a significant influence on personal exposure levels, though explained only up to 20% of the variability of road transport user exposure levels. The occurrence of higher wind speeds was strongly associated with a decrease in personal exposure levels; a 1.5–2.0 fold difference in exposure level concentrations was estimated between the 10th and 90th percentiles of wind speed. Route was a significant factor, whilst mode was not a significant factor in the street microenvironment (between bicycle, bus and car modes); models incorporating route and mode, as well as wind speed, explained approximately 35% of the variability in PM_2.5 exposure levels. Personal exposure levels were reasonably correlated with urban background FSM concentrations, for fixed-route road mode (bicycle, bus and car) exposure level concentrations, r=0.27 (p<0.01) and for commuter cyclists’ exposure level concentrations r=0.58 (p<0.01).     

Characterisation of PM10 and PM2.5 particulate matter in the ambient air of Milan (Italy)

24-h simultaneous samplings of PM10 and PM2.5 particulate matter (PM) have been carried out during the period December 1997–September 1998 in the central urban area of Milan. The mass concentrations of the two fractions showed significant daily variations linked to different thermodynamic conditions of the planetary boundary layer (PBL) and characterised by higher values during wintertime. The elemental composition, determined by energy dispersive X-ray fluorescence technique, was quite different in the two fractions: in the finer one the presence of elements with crustal origin is reduced while the anthropogenic elements, with a relevant environmental and health impact, appear to be enriched. The composition data allowed a quantification of two major components of the atmospheric particulate: sulphates (mainly of secondary origin) and particles with crustal origin. An important but unmeasured component is likely constituted by organic and elemental carbon compounds.The multivariate analysis of elements, gaseous pollutants and mass concentration data-sets leads to the identification of four main sources contributing to PM10 and PM2.5 composition: vehicles exhaust emissions, resuspended crustal dust, secondary sulphates and industrial emissions. The existence of a possible background component with non-local origin is also suggested.     

人体室内外PM_(2.5)吸入暴露研究

介绍了室内外空气颗粒物吸入暴露的评价方法,选择PM2.5作为检测评价的对象,初步评价了上海市某区不同年龄段人员的PM2.5暴露水平。结果表明:(1)成人和老人的全年日平均PM2.5吸入暴露量均较高,并且成人的全年日平均PM2.5吸入暴露量变化曲线和儿童相似。(2)老人室内PM2.5吸入暴露量要明显高于室外,其主要原因是老人在室内时间较长。儿童和成人的室外PM2.5吸入暴露量高于室内。(3)不同人员的年平均PM2.5吸入暴露量的排序为成人老人儿童,其年平均PM2.5吸入暴露量分别为1.141、1.046、0.935mg。     

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。     

Source identification of personal exposure to fine particulate matter using organic tracers

Personal exposure to fine particulate matter (PM2.5) is due to both indoor and outdoor sources. Contributions of sources to personal exposure can be quite different from those observed at ambient sampling locations. The primary goal of this study was to investigate the effectiveness of using trace organic speciation data to help identify sources influencing PM2.5 exposure concentrations. Sixty-four 24-h PM2.5 samples were obtained on seven different subjects in and around Boulder, CO. The exposure samples were analyzed for PM2.5 mass, elemental and organic carbon, organic tracer compounds, water-soluble metals, ammonia, and nitrate. This study is the first to measure a broad distribution of organic tracer compounds in PM2.5 personal samples. PM2.5 mass exposure concentrations averaged 8.4 μg m^?3. Organic carbon was the dominant constituent of the PM2.5 mass. Forty-four organic species and 19 water-soluble metals were quantifiable in more than half of the samples. Fifty-four organic species and 16 water-soluble metals had measurement signal-to-noise ratios larger than two after blank subtraction.The dataset was analyzed by Principal Component Analysis (PCA) to determine the factors that account for the greatest variance. Eight significant factors were identified; each factor was matched to its likely source based primarily on the marker species that loaded the factor. The results were consistent with the expectation that multiple marker species for the same source loaded the same factor. Meat cooking was an important source of variability. The factor that represents meat cooking was highly correlated with organic carbon concentrations (r = 0.84). The correlation between ambient PM2.5 and PM2.5 exposure was relatively weak (r = 0.15). Time participants spent performing various activities was generally not well correlated with PCA factor scores, likely because activity duration does not measure emissions intensity. The PCA results demonstrate that organic tracers can aid in identifying factors that influence personal exposures to PM2.5.     

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.     

Assessment of personal integrated exposure to fine particulate matter of urban residents in Hong Kong

Metropolitan residents are concerned about their exposure to airborne pollutants. But establishing these exposures is challenging. A compact personal exposure kit (PEK) was developed to evaluate personal integrated exposure (PIE) from time-resolved data to particulate matter with aerodynamic diameter less than 2.5 μm (PM_2.5) in five microenvironments, including office, home, commuting, other indoor activities (other than home and office), and outdoor activities experienced both on weekdays and weekends. The study was conducted in Hong Kong. The PEK measured PM_2.5, reported location and several other factors, stored collected data, as well as reported the data back to the investigators using global system for mobile communication (GSM) telemetry. Generally, PM_2.5 concentrations in office microenvironment were found to be the smallest (13.0 μg/m³), whereas the largest PM_2.5 concentration microenvironments were experienced during outdoor activities (54.4 μg/m³). Participants spent more than 85% of their time indoors, including in offices, homes, and other public indoor venues. On average, 42% and 81% of the time were spent in homes, which contributed 52% and 79% of PIE (during weekdays and weekends, respectively), suggesting that improvement of air quality in homes may reduce overall exposures and indicating the need for actions to mitigate possible public health burdens in Hong Kong. This study also found that various indoor/outdoor microenvironments experienced by urban office workers cannot be accurately represented by general urban air quality data reported from the regulatory monitoring. Such personalized air quality information, especially while in transit or in offices and homes, may provide improved information on population exposures to air pollution.

Implications: A newly developed personal exposure kit (PEK) was used to monitor PM_2.5 exposure of metropolitan citizens in their daily life. Different microenvironments and time durations caused various personal integrated exposure (PIE). The stationary monitoring method for PIE was also compared and evaluated with PEK. Positive protection actions can be taken after understanding the major contribution to PM_2.5 exposure.     

Air quality at Santiago,Chile: a box modeling approach II. PM2.5, coarse and PM10 particulate matter fractions

Ambient monitored data at Santiago, Chile, are analyzed using box models with the goal of assessing contributions of different economic activities to air pollution levels. The box modeling approach was applied to PM₁₀, PM_2.5 and coarse (PM₁₀–PM_2.5) particulate matter (PM) fractions; the period analyzed is 1989–1999. A linear model for each PM fraction was obtained, having as independent variables CO and SO₂ concentrations, plus a term proportional to (wind speed)⁻¹ that lumps together non-combustion emissions and secondary generation terms; wet scavenging is included as another independent variable. Model identification results show good agreement for the different parameters across monitoring stations. The washout ratios and scavenging coefficients agree with data published in the literature, being higher for the coarse PM fraction. The CO and SO₂ coefficients fitted for 1989–1995 agree with a priori estimates for the same period. Background estimates for the PM fractions are in agreement with measurement campaigns in upwind sites. Results show that transportation sources have become the dominant contributors to ambient PM levels, while stationary sources have decreased their contributions in the last years. The relative importance of mobile sources to PM_2.5 ambient concentrations has doubled in the last 10 years, whereas stationary sources have reduced their relative contributions to half the value in the early 1990s. Model estimates of regional background of PM_2.5 and PM₁₀ have decreased 50% and 22% in the last decade, respectively; coarse background has shown no significant change. The final conclusion is that there is room and need for a more intensive emission reduction strategy for Santiago, focusing on mobile sources. The approach pursued in this work is feasible for cities or regions where comprehensive, transport and chemistry models are not available yet, but estimates of air quality contributions are needed for policy purposes. The methodology requires data on ambient air quality measurements and surface meteorology.     

Outdoor versus indoor contributions to indoor particulate matter (PM) determined by mass balance methods

This study compares an indoor-outdoor air-exchange mass balance model (IO model) with a chemical mass balance (CMB) model. The models were used to determine the contribution of outdoor sources and indoor resuspension activities to indoor particulate matter (PM) concentrations. Simultaneous indoor and outdoor measurements of PM concentration, chemical composition, and air-exchange rate were made for five consecutive days at a single-family residence using particle counters, nephelometers, and filter samples of integrated PM with an aerodynamic diameter of less than or equal to 2.5 microm (PM2.5) and PM with an aerodynamic diameter of less than or equal to 5 microm (PM5). Chemical compositions were determined by inductively coupled plasma mass-spectrometry. During three high-activity days, prescribed activities, such as cleaning and walking, were conducted over a period of 4-6 hr. For the remaining two days, indoor activities were minimal. Indoor sources accounted for 60-89% of the PM2.5 and more than 90% of the PM5 for the high-activity days. For the minimal-activity days, indoor sources accounted for 27-47% of PM2.5 and 44-60% of the PM5. Good agreement was found between the two mass balance methods. Indoor PM2.5 originating outdoors averaged 53% of outdoor concentrations.     

Personal exposure to fine particulate matter concentrations in central business district of a tropical coastal city

In the present study, personal exposure to fine particulate matter (particulate matter with an aerodynamic diameter <2.5 μm [PM_2.5]) concentrations in an urban hotspot (central business district [CBD]) was investigated. The PM monitoring campaigns were carried out at an urban hotspot from June to October 2015. The personal exposure monitoring was performed during three different time periods, i.e., morning (8 a.m.?9 a.m.), afternoon (12.30 p.m.–1.30 p.m.), and evening (4 p.m.–5 p.m.), to cover both the peak and lean hour activities of the CBD. The median PM_2.5 concentrations were 38.1, 34.9, and 40.4 µg/m³ during the morning, afternoon, and evening hours on the weekends. During weekdays, the median PM_2.5 concentrations were 59.5, 29.6, and 36.6 µg/m³ in the morning, afternoon, and evening hours, respectively. It was observed that the combined effect of traffic emissions, complex land use, and micrometeorological conditions created localized air pollution hotspots. Furthermore, the total PM_2.5 lung dose levels for an exposure duration of 1 hr were 8.7 ± 5.7 and 12.3 ± 5.2 µg at CBD during weekends and weekdays, respectively, as compared with 2.5 ± 0.8 µg at the urban background (UB). This study emphasizes the need for mobile measurement for short-term personal exposure assessment complementing the fixed air quality monitoring.

Implications: Personal exposure monitoring at an urban hotspot indicated space and time variation in PM concentrations that is not captured by the fixed air quality monitoring networks. The short-term exposure to higher concentrations can have a significant impact on health that need to be considered for the health risk–based air quality management. The study emphasizes the need of hotspot-based monitoring complementing the already existing fixed air quality monitoring in urban areas. The personal exposure patterns at hotspots can provide additional insight into sustainable urban planning.     

Determinants of indoor and personal exposure to PM2.5 of indoor and outdoor origin during the RIOPA study

Effects of physical/environmental factors on fine particle (PM_2.5) exposure, outdoor-to-indoor transport and air exchange rate (AER) were examined. The fraction of ambient PM_2.5 found indoors (F_INF) and the fraction to which people are exposed (α) modify personal exposure to ambient PM_2.5. Because F_INF, α, and AER are infrequently measured, some have used air conditioning (AC) as a modifier of ambient PM_2.5 exposure. We found no single variable that was a good predictor of AER. About 50% and 40% of the variation in F_INF and α, respectively, was explained by AER and other activity variables. AER alone explained 36% and 24% of the variations in F_INF and α, respectively. Each other predictor, including Central AC Operation, accounted for less than 4% of the variation. This highlights the importance of AER measurements to predict F_INF and α. Evidence presented suggests that outdoor temperature and home ventilation features affect particle losses as well as AER, and the effects differ.Total personal exposures to PM_2.5 mass/species were reconstructed using personal activity and microenvironmental methods, and compared to direct personal measurement. Outdoor concentration was the dominant predictor of (partial R² = 30–70%) and the largest contributor to (20–90%) indoor and personal exposures for PM_2.5 mass and most species. Several activities had a dramatic impact on personal PM_2.5 mass/species exposures for the few study participants exposed to or engaged in them, including smoking and woodworking. Incorporating personal activities (in addition to outdoor PM_2.5) improved the predictive power of the personal activity model for PM_2.5 mass/species; more detailed information about personal activities and indoor sources is needed for further improvement (especially for Ca, K, OC). Adequate accounting for particle penetration and persistence indoors and for exposure to non-ambient sources could potentially increase the power of epidemiological analyses linking health effects to particulate exposures.     

Assessment of human exposure to ambient particulate matter

Recent epidemiological studies have consistently shown that the acute mortality effects of high concentrations of ambient particulate matter (PM), documented in historic air pollution episodes, may also be occurring at the low to moderate concentrations of ambient PM found in modern urban areas. In London in December 1952, the unexpected deaths due to PM exposure could be identified and counted as integers by the coroners. In modern times, the PM-related deaths cannot be as readily identified, and they can only be inferred as fractional average daily increases in mortality rates using sophisticated statistical filtering and analyses of the air quality and mortality data. The causality of the relationship between exposure to ambient PM and acute mortality at these lower modern PM concentrations has been questioned because of a perception that there is little significant correlation in time between the ambient PM concentrations and measured personal exposure to PM from all sources (ambient PM plus indoor-generated PM). This article shows that the critical factor supporting the plausibility of a linear PM mortality relationship is the expected high correlation in time of people's exposure to PM of ambient origin with measured ambient PM concentrations, as used in the epidemiological time series studies. The presence of indoor and personal sources of PM masks this underlying relationship, leading to confusion in the scientific literature about the strong underlying temporal relationship between personal exposure to PM of ambient origin and ambient PM concentration. The authors show that the sources of PM of non-ambient origin operate independently of the ambient PM concentrations, so that the mortality effect of non-ambient PM, if any, must be independent of the effects of the ambient PM exposures.     

Exposure to particulate matter in traffic: A comparison of cyclists and car passengers

Emerging evidence suggests that short episodes of high exposure to air pollution occur while commuting. These events can result in potentially adverse health effects. We present a quantification of the exposure of car passengers and cyclists to particulate matter (PM). We have simultaneously measured concentrations (PNC, PM2.5 and PM10) and ventilatory parameters (minute ventilation (VE), breathing frequency and tidal volume) in three Belgian locations (Brussels, Louvain-la-Neuve and Mol) for 55 persons (38 male and 17 female). Subjects were first driven by car and then cycled along identical routes in a pairwise design. Concentrations and lung deposition of PNC and PM mass were compared between biking trips and car trips.Mean bicycle/car ratios for PNC and PM are close to 1 and rarely significant. The size and magnitude of the differences in concentrations depend on the location which confirms similar inconsistencies reported in literature. On the other hand, the results from this study demonstrate that bicycle/car differences for inhaled quantities and lung deposited dose are large and consistent across locations. These differences are caused by increased VE in cyclists which significantly increases their exposure to traffic exhaust. The VE while riding a bicycle is 4.3 times higher compared to car passengers. This aspect has been ignored or severely underestimated in previous studies. Integrated health risk evaluations of transport modes or cycling policies should therefore use exposure estimates rather than concentrations.     

Application of a microscale emission factor model for particulate matter to calculate vehicle-generated contributions to fine particulate emissions

This paper discusses the evaluation and application of a new generation of particulate matter (PM) emission factor model (MicroFacPM). MicroFacPM that was evaluated in Tuscarora Mountain Tunnel, Pennsylvania Turnpike, PA shows good agreement between measured and modeled emissions. MicroFacPM application is presented to the vehicle traffic on the main approach road to the Ambassador Bridge, which is one of the most important international border entry points in North America, connecting Detroit, MI, with Windsor, Ontario, Canada. An increase in border security has forced heavy-duty diesel vehicles to line up for several kilometers through the city of Windsor causing concern about elevated concentrations of ambient PM. MicroFacPM has been developed to model vehicle-generated PM (fine [PM2.5] and coarse < or = 10 microm [PM10]) from the on-road vehicle fleet, which in this case includes traffic at very low speeds (10 km/h). The Windsor case study gives vehicle generated PM2.5 sources and their breakdown by vehicle age and class. It shows that the primary sources of vehicle-generated PM2.5 emissions are the late-model heavy-duty diesel vehicles. We also applied CALINE4 and AERMOD in conjunction with MicroFacPM, using Canadian traffic and climate conditions, to describe the vehicle-generated PM2.5 dispersion near this roadway during the month of May in 2003.