Designing ambient particulate matter monitoring program for source apportionment study by receptor modeling

Particulate matter (PM) receptor modeling requires specific intensive input data that is always a challenge to produce cost effectively. A well-designed monitoring program is important to collect such PM ambient data in urban areas with diverse and densely distributed sources. This paper presents a general framework for designing such a monitoring program while emphasizing appropriate quality assurance and quality control elements that are particularly applicable where limited resources are available. Topics for discussion include selection of monitoring sites, sampling and analytical techniques, and the uncertainty estimation for ambient concentration input data. The design framework is illustrated by a case study of a monitoring program for PM source apportionment in the Bangkok Metropolitan Region in which 24-h fine and coarse PM samples were collected using two collocated dichotomous samplers. Comparison between black carbon measurements by Smoke Stain reflectometry and Thermal Optical Transmittance method is highlighted.     

Theoretical study of the impact of particulate matter gravitational settling on ambient coarse particulate matter monitoring for agricultural emissions

The particle size distributions (PSDs) of particulate matter (PM) in the downwind plume from simulated sources of a cotton gin were analyzed to determine the impact of PM settling on PM monitoring. The PSD of PM in a plume varies as a function of gravitational settling. Gravitational settling has a greater impact on the downwind PSD from sources with PSDs having larger mass median diameters (MMDs). The change in PSD is a function of the source PSD of emitted PM, wind speed, and downwind distance. Both MMD and geometric standard deviation (GSD) in the downwind plume decrease with an increase in downwind distance and source MMD. The larger the source MMD, the greater the change in the downwind MMD and GSD. Also, the greater the distance from the source to the sampler, the greater the change in the downwind MMD and GSD. Variations of the PSD in the downwind plume significantly impact PM10 sampling errors associated with the U.S. Environmental Protection Agency (EPA) PM10 samplers. For the emission sources with MMD > 10 microm, the PM10 oversampling rate increases with an increase in downwind distance caused by the decrease of GSD of the PSD in the downwind plume. Gravitational settling of particles does not help reduce the oversampling problems associated with the EPA PM10 sampler. Furthermore, oversampling rates decrease with an increase of the wind speed.     

Natural emissions for regional modeling of background ozone and particulate matter and impacts on emissions control strategies

Natural emissions adopted in current regional air quality modeling are updated to better describe natural background ozone and PM concentrations for North America. The revised natural emissions include organosulfur from the ocean, NO from lightning, sea salt, biogenic secondary organic aerosol (SOA) precursors, and pre-industrial levels of background methane. The model algorithm for SOA formation was also revised. Natural background ozone concentrations increase by up to 4 ppb in annual average over the southeastern US and Gulf of Mexico due to added NO from lightning while the revised biogenic emissions produced less ozone in the central and western US. Natural PM_2.5 concentrations generally increased with the revised natural emissions. Future year (2018) simulations were conducted for several anthropogenic emission reduction scenarios to assess the impact of the revised natural emissions on anthropogenic emission control strategies. Overall, the revised natural emissions did not significantly alter the ozone responses to the emissions reductions in 2018. With revised natural emissions, ozone concentrations were slightly less sensitive to reducing NOx in the southeastern US than with the current natural emissions due to higher NO from lightning. The revised natural emissions have little impact on modeled PM_2.5 responses to anthropogenic emission reductions. However, there are substantial uncertainties in current representations of natural sources in air quality models and we recommend that further study is needed to refine these representations.     

Chemical characterization and source apportionment of fine and coarse particulate matter in Lahore,Pakistan

Lahore, Pakistan is an emerging megacity that is heavily polluted with high levels of particle air pollution. In this study, respirable particulate matter (PM_2.5 and PM₁₀) were collected every sixth day in Lahore from 12 January 2007 to 19 January 2008. Ambient aerosol was characterized using well-established chemical methods for mass, organic carbon (OC), elemental carbon (EC), ionic species (sulfate, nitrate, chloride, ammonium, sodium, calcium, and potassium), and organic species. The annual average concentration (±one standard deviation) of PM_2.5 was 194 ± 94 μg m^?3 and PM₁₀ was 336 ± 135 μg m^?3. Coarse aerosol (PM_10?2.5) was dominated by crustal sources like dust (74 ± 16%, annual average ± one standard deviation), whereas fine particles were dominated by carbonaceous aerosol (organic matter and elemental carbon, 61 ± 17%). Organic tracer species were used to identify sources of PM_2.5 OC and chemical mass balance (CMB) modeling was used to estimate relative source contributions. On an annual basis, non-catalyzed motor vehicles accounted for more than half of primary OC (53 ± 19%). Lesser sources included biomass burning (10 ± 5%) and the combined source of diesel engines and residual fuel oil combustion (6 ± 2%). Secondary organic aerosol (SOA) was an important contributor to ambient OC, particularly during the winter when secondary processing of aerosol species during fog episodes was expected. Coal combustion alone contributed a small percentage of organic aerosol (1.9 ± 0.3%), but showed strong linear correlation with unidentified sources of OC that contributed more significantly (27 ± 16%). Brick kilns, where coal and other low quality fuels are burned together, are suggested as the most probable origins of unapportioned OC. The chemical profiling of emissions from brick kilns and other sources unique to Lahore would contribute to a better understanding of OC sources in this megacity.     

Investigation and modeling of the residential infiltration of fine particulate matter in Beijing,China

The objective of this study was to estimate the residential infiltration factor (Finf) of fine particulate matter (PM_2.5) and to develop models to predict PM_2.5 Finf in Beijing. Eighty-eight paired indoor–outdoor PM_2.5 samples were collected by Teflon filters for seven consecutive days during both non-heating and heating seasons (from a total of 55 families between August, 2013 and February, 2014). The mass concentrations of PM_2.5 were measured by gravimetric method, and elemental concentrations of sulfur in filter deposits were determined by energy-dispersive x-ray fluorescence (ED-XRF) spectrometry. PM_2.5 Finf was estimated as the indoor/outdoor sulfur ratio. Multiple linear regression was used to construct Finf predicting models. The residential PM_2.5 Finf in non-heating season (0.70 ± 0.21, median = 0.78, n = 43) was significantly greater than in heating season (0.54 ± 0.18, median = 0.52, n = 45, p < 0.001). Outdoor temperature, window width, frequency of window opening, and air conditioner use were the most important predictors during non-heating season, which could explain 57% variations across residences, while the outdoor temperature was the only predictor identified in heating season, which could explain 18% variations across residences. The substantial variations of PM_2.5 Finf between seasons and among residences found in this study highlight the importance of incorporating Finf into exposure assessment in epidemiological studies of air pollution and human health in Beijing. The Finf predicting models developed in this study hold promise for incorporating PM_2.5 Finf into large epidemiology studies, thereby reducing exposure misclassification.

Implications: Failure to consider the differences between indoor and outdoor PM_2.5 may contribute to exposure misclassification in epidemiological studies estimating exposure from a central site measurement. This study was conducted in Beijing to investigate residential PM_2.5 infiltration factor and to develop a localized predictive model in both nonheating and heating seasons. High variations of PM_2.5 infiltration factor between the two seasons and across homes within each season were found, highlighting the importance of including infiltration factor in the assessment of exposure to PM_2.5 of outdoor origin in epidemiological studies. Localized predictive models for PM_2.5 infiltration factor were also developed.     

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.     

Simulating the fine and coarse inorganic particulate matter concentrations in a polluted megacity

A three dimensional chemical transport model (PMCAMx) is applied to the Mexico City Metropolitan Area (MCMA) in order to simulate the chemical composition and mass of the major PM₁ (fine) and PM_1–10 (coarse) inorganic components and determine the effect of mineral dust on their formation. The aerosol thermodynamic model ISORROPIA-II is used to explicitly simulate the effect of Ca, Mg, and K from dust on semi-volatile partitioning and water uptake. The hybrid approach is applied to simulate the inorganic components, assuming that the smallest particles are in thermodynamic equilibrium, while describing the mass transfer to and from the larger ones. The official MCMA 2004 emissions inventory with improved dust and NaCl emissions is used. The comparison between the model predictions and measurements during a week of April of 2003 at Centro Nacional de Investigacion y Capacitacion Ambiental (CENICA) “Supersite” shows that the model reproduces reasonably well the fine mode composition and its diurnal variation. Sulfate predicted levels are relatively uniform in the area (approximately 3 μg m^?3), while ammonium nitrate peaks in Mexico City (approximately 7 μg m^?3) and its concentration rapidly decreases due to dilution and evaporation away from the urban area. In areas of high dust concentrations, the associated alkalinity is predicted to increase the concentration of nitrate, chloride and ammonium in the coarse mode by up to 2 μg m^?3 (a factor of 10), 0.4 μg m^?3, and 0.6 μg m^?3 (75%), respectively. The predicted ammonium nitrate levels inside Mexico City for this period are sensitive to the physical state (solid versus liquid) of the particles during periods with RH less than 50%.     

Apportionment of ambient primary and secondary fine particulate matter at the Pittsburgh National Energy Laboratory particulate matter characterization site using positive matrix factorization and a potential source contributions function analysis

Fine particulate matter (PM2.5) concentrations associated with 202 24-hr samples collected at the National Energy Technology Laboratory (NETL) particulate matter (PM) characterization site in south Pittsburgh from October 1999 through September 2001 were used to apportion PM2.5 into primary and secondary contributions using Positive Matrix Factorization (PMF2). Input included the concentrations of PM2.5 mass determined with a Federal Reference Method (FRM) sampler, semi-volatile PM2.5 organic material, elemental carbon (EC), and trace element components of PM2.5. A total of 11 factors were identified. The results of potential source contributions function (PSCF) analysis using PMF2 factors and HYSPLIT-calculated back-trajectories were used to identify those factors associated with specific meteorological transport conditions. The 11 factors were identified as being associated with emissions from various specific regions and facilities including crustal material, gasoline combustion, diesel combustion, and three nearby sources high in trace metals. Three sources associated with transport from coal-fired power plants to the southeast, a combination of point sources to the northwest, and a steel mill and associated sources to the west were identified. In addition, two secondary-material-dominated sources were identified, one was associated with secondary products of local emissions and one was dominated by secondary ammonium sulfate transported to the NETL site from the west and southwest. Of these 11 factors, the four largest contributors to PM2.5 were the secondary transported material (dominated by ammonium sulfate) (47%), local secondary material (19%), diesel combustion emissions (10%), and gasoline combustion emissions (8%). The other seven factors accounted for the remaining 16% of the PM2.5 mass. The findings are consistent with the major source of PM2.5 in the Pittsburgh area being dominated by ammonium sulfate from distant transport and so decoupled from local activity emitting organic pollutants in the metropolitan area. In contrast, the major local secondary sources are dominated by organic material.     

Effects of wind direction on coarse and fine particulate matter concentrations in southeast Kansas

Field data for coarse particulate matter ([PM] PM10) and fine particulate matter (PM2.5) were collected at selected sites in Southeast Kansas from March 1999 to October 2000, using portable MiniVol particulate samplers. The purpose was to assess the influence on air quality of four industrial facilities that burn hazardous waste in the area located in the communities of Chanute, Independence, Fredonia, and Coffeyville. Both spatial and temporal variation were observed in the data. Variation because of sampling site was found to be statistically significant for PM10 but not for PM2.5. PM10 concentrations were typically slightly higher at sites located within the four study communities than at background sites. Sampling sites were located north and south of the four targeted sources to provide upwind and downwind monitoring pairs. No statistically significant differences were found between upwind and downwind samples for either PM10 or PM2.5, indicating that the targeted sources did not contribute significantly to PM concentrations. Wind direction can frequently contribute to temporal variation in air pollutant concentrations and was investigated in this study. Sampling days were divided into four classifications: predominantly south winds, predominantly north winds, calm/variable winds, and winds from other directions. The effect of wind direction was found to be statistically significant for both PM10 and PM2.5. For both size ranges, PM concentrations were typically highest on days with predominantly south winds; days with calm/variable winds generally produced higher concentrations than did those with predominantly north winds or those with winds from "other" directions. The significant effect of wind direction suggests that regional sources may exert a large influence on PM concentrations in the area.     

杭州市空气颗粒物污染特征及变化规律研究

根据2006—2010年杭州市空气颗粒物的监测数据及2002、2006、2008年空气颗粒物来源解析结果,对杭州市空气颗粒物浓度、化学组分与污染来源等特征的变化规律进行分析,以期为空气颗粒物污染控制提供决策依据。结果表明,近年来杭州市PM10浓度有所下降,但一类功能区PM10仍超出《环境空气质量标准》(GB 3095—1996)的要求(≤0.04mg/m3),杭州市空气颗粒物污染以细颗粒物为主,空气颗粒物的二次转化、机动车尾气尘等产生的二次粒子污染相对严重;煤烟尘对杭州市PM10的贡献率下降明显,城市扬尘、二次粒子和机动车尾气尘对PM10的贡献率有所增加,是杭州市PM10的主要来源。     

Airborne particulate matter from livestock production systems: A review of an air pollution problem

Livestock housing is an important source of emissions of particulate matter (PM). High concentrations of PM can threaten the environment, as well as the health and welfare of humans and animals. Particulate matter in livestock houses is mainly coarse, primary in origin, and organic; it can adsorb and contain gases, odorous compounds, and micro-organisms, which can enhance its biological effect. Levels of PM in livestock houses are high, influenced by kind of housing and feeding, animal type, and environmental factors. Improved knowledge on particle morphology, primarily size, composition, levels, and the factors influencing these can be useful to identify and quantify sources of PM more accurately, to evaluate their effects, and to propose adequate abatement strategies in livestock houses. This paper reviews the state-of-the-art of PM in and from livestock production systems. Future research to characterize and control PM in livestock houses is discussed.     

Levels of total suspended particulate matter and major trace elements in Kosovo: a source identification and apportionment study

Concentration levels of total suspended particles (TSP) and 27 major, minor and trace elemental components were determined at four sites in Kosovo through a 1-year survey (January-December 2002). Ambient concentrations were evaluated in comparison to limit values. The origin of elemental TSP constituents was investigated by calculating enrichment factors and diagnostic ratios. Multivariate statistics, such as hierarchical cluster analysis and factor analysis, were also employed to identify emission sources. A multivariate statistical receptor model (Absolute Principal Component Analysis, APCA) was applied to quantify source contributions. Soil dust, cement production, vehicular emissions, brake wear, and fuel combustion were identified as major sources with variable contributions at the four sampling sites.     

Fugitive dust emission source profiles and assessment of selected control strategies for particulate matter at gravel processing sites in Taiwan

Particles emitted from gravel processing sites are one contributor to worsening air quality in Taiwan. Major pollution sources at gravel processing sites include gravel and sand piles, unpaved roads, material crushers, and bare ground. This study analyzed fugitive dust emission characteristics at each pollution source using several types of particle samplers, including total suspended particulates (TSP), suspended particulate (PM10), fine suspended particulate (PM2.5), particulate sizer, and dust-fall collectors. Furthermore, silt content and moisture in the gravel were measured to develop particulate emission factors. The results showed that TSP (< 100 microm) concentrations at the boundary of gravel sites ranged from 280 to 1290 microg/m3, which clearly exceeds the Taiwan hourly air quality standard of 500 microg/m3. Moreover, PM10 concentrations, ranging from 135 to 550 microg/m3, were also above the daily air quality standard of 125 microg/m3 and approximately 1.2 and 1.5 times the PM2.5 concentrations, ranging from 105 to 470 microg/m3. The size distribution analysis reveals that mass mean diameter and geometric standard deviation ranged from 3.2 to 5.7 microm and from 2.82 to 5.51, respectively. In this study, spraying surfactant was the most effective control strategy to abate windblown dust from unpaved roads, having a control efficiency of approximately 93%, which is significantly higher than using paved road strategies with a control efficiency of approximately 45%. For paved roads, wet suppression provided the best dust control efficiencies ranging from 50 to 83%. Re-vegetation of disturbed ground had dust control efficiencies ranging from 48 to 64%.     

Guidance for the performance evaluation of three-dimensional air quality modeling systems for particulate matter and visibility

Guidance for the performance evaluation of three-dimensional air quality modeling systems for particulate matter and visibility is presented. Four levels are considered: operational, diagnostic, mechanistic, and probabilistic evaluations. First, a comprehensive model evaluation should be conducted in at least two distinct geographical locations and for several meteorological episodes. Next, streamlined evaluations can be conducted for other similar applications if the comprehensive evaluation is deemed satisfactory. In all cases, the operational evaluation alone is insufficient, and some diagnostic evaluation must always be carried out. Recommendations are provided for designing field measurement programs that can provide the data needed for such model performance evaluations.     

杭州市主城区大气颗粒物的酸缓冲能力及来源解析

通过酸缓冲能力的测定实验,研究杭州市主城区大气颗粒物的酸缓冲能力,并利用二重源解析技术,解析了大气颗粒物中碱性组分的来源.结果表明,杭州市主城区大气颗粒物呈弱碱性,对降水酸度有一定的缓冲作用,但作用较小.总体而言,TSP与PM10的酸缓冲能力与其浓度呈负相关,但相关关系不明显,TSP的酸缓冲能力比PM10强.Ca是影响大气颗粒物酸缓冲能力的关键化学组分.TSP的酸缓冲能力主要来自建筑水泥尘.     

Association between particulate matter pollution and the incidence of mumps in 31 provinces from China

Environmental Science and Pollution Research - Previous studies have found that particulate matter (PM) pollution is a risk factor for respiratory disease by affecting body’s immunity and...     

Numerical study of the effect of ventilation pattern on coarse, fine, and very fine particulate matter removal in partitioned indoor environment

An indoor size-dependent particulate matter (PM) transport approach is developed to investigate coarse PM (PM10), fine PM (PM2.5), and very fine PM (PM1) removal behaviors in a ventilated partitioned indoor environment. The approach adopts the Eulerian large eddy simulation of turbulent flow and the Lagrangian particle trajectory tracking to solve the continuous airflow phase and the discrete particle phase, respectively. Model verification, including sensitivity tests of grid resolution and particle numbers, is conducted by comparison with the full-size experiments conducted previously. Good agreement with the measured mass concentrations is found. Numerical scenario simulations of the effect of ventilation patterns on PM removal are performed by using three common ventilation patterns (piston displacement, mixing, and cross-flow displacement ventilation) with a measured indoor PM10 profile in the Taipei metropolis as the initial condition. The temporal variations of suspended PM10, PM2.5, and PM1 mass concentrations and particle removal mechanisms are discussed. The simulated results show that for all the of the three ventilation patterns, PM2.5 and PM1 are much more difficult to remove than PM10. From the purpose of health protection for indoor occupants, it is not enough to only use the PM10 level as the indoor PM index. Indoor PM2.5 and PM1 levels should be also considered. Cross-flow displacement ventilation is more effective to remove all PM10, PM2.5, and PM1 than the other ventilation patterns. Displacement ventilation would result in more escaped particles and less deposited particles than mixing ventilation.     

A review of effects and control methods of particulate matter in animal indoor environments

This paper reviews the effects and control methods for particulate matter (PM) in animal indoor environments. PM in animal indoor environments represents a particular threat to the health of countless animals and millions of workers around the world. Because air in animal facilities has a higher portion of biological content than does air in other environments, the adverse health impact is much greater than it is for the same amount of PM in other environments. Source control, ventilation, and internal air cleaning can reduce PM concentration in animal indoor environments. Source control is typically the most economical method for PM control. Ventilation is the most widely applied technology, although uncertainties remain as to its effectiveness for PM control. Most internal air cleaners require frequent maintenance because of the high concentration and stickiness of PM in animal environments. Filtration is the most well-studied and widely used technology for internal air cleaning because of its low capital cost and high efficiency. Several trials using electrostatic precipitators have shown efficiencies of approximately 50% in removing PM concentration, but improvements are needed before they can be used widely. No report using wet collectors or centrifugals as internal air cleaners has been found.     

Assessment of traffic-related air pollution in the urban streets before and during the 2008 Beijing Olympic Games traffic control period

In order to investigate the air quality and the abatement of traffic-related pollution during the 2008 Olympic Games, we select 12 avenues in the urban area of Beijing to calculate the concentrations of PM₁₀, CO, NO₂ and O₃ before and during the Olympic traffic controlling days, with the OSPM model.Through comparing the modeled results with the measurement results on a representative street, the OSPM model is validated as sufficient to predict the average concentrations of these pollutants at street level, and also reflects their daily variations well, i.e. CO presents the similar double peaks as the traffic flow, PM₁₀ concentration is influenced by other sources. Meanwhile, the model predicts O₃ to stay less during the daytime and ascend in the night, just opposite to NO₂, which reveals the impact of photochemical reactions. In addition, the predicted concentrations on the windward side often exceed the leeward side, indicating the impact of the special street shape, as well as the wind.The comparison between the predicted street concentrations before and during the Olympic traffic control period shows that the overall on-road air quality was improved effectively, due to the 32.3% traffic flow reduction. The concentrations of PM₁₀, CO and NO₂ have reduced from 142.6 μg m⁻³, 3.02 mg m⁻³ and 118.7 μg m⁻³ to 102.0 μg m⁻³, 2.43 mg m⁻³ and 104.1 μg m⁻³. However, the different pollutants show diverse changes after the traffic control. PM₁₀ decreases most, and the reduction effect focusing on the first half-day even clears the morning peak, whereas CO and NO₂ have even reductions to minify the daily fluctuations on the whole. Opposite to the other pollutants, ozone shows an increase of concentration. The average reduction rate of PM₁₀, CO, NO₂ and O₃ are respectively 28%, 19.3%, 12.3% and −25.2%. Furthermore, the streets in east, west, south and north areas present different air quality improvements, probably induced by the varied background pollution in different regions around Beijing, along with the impact of wind force. This finding suggests the pollution control in the surrounding regions, not only in the urban area.     

Sampling technologies and air pollution control devices for gaseous and particulate arsenic: a review

Direct measurement of arsenic release requires a good sampling and analysis procedure in order to capture and detect the total amount of metals emitted. The literature is extensively reviewed in order to evaluate the efficiency of full field-scale and laboratory scale techniques for capturing particulate and gaseous emissions of arsenic from the thermo-chemical treatment of different sources of arsenic. Furthermore, trace arsenic concentrations in ambient air, national standard sampling methods and arsenic analysis methods are considered. Besides sampling techniques, the use of sorbents is also reviewed with respect to both approaches (1) to prevent the metals from exiting with the flue gas and (2) to react or combine with the metals in order to be collected in air pollution control systems. The most important conclusion is that submicron arsenic fumes are difficult to control in conventional air pollution control devices. Complete capture of the arsenic species requires a combination of particle control and vapour control devices.