To investigate the characteristics and contributions of the sources of fine particulate matter with a size of up to 2.5 μm (PM2.5) during the period when pollution events could easily occur in ...Taoyuan aerotropolis, Taiwan, this study conducted sampling at three-day intervals from September 2014 to January 2015. Based on the mass concentration of PM2.5, the sampling days were classified into high PM2.5 concentration event days (PM2.5>35 μg m−3) and non-event days (PM2.5<35 μg m−3). In addition, the chemical species, including water-soluble inorganic ions, carbonaceous components, and metal elements, were analyzed. The sources of pollution and their contributions were estimated using the positive matrix factorization (PMF) model. Furthermore, the effect of the weather type on the measurement results was also explored based on wind field conditions. The mass fractions of Cl− and NO3− increased when a high PM2.5 concentration event occurred, and they were also higher under local emitted conditions than under long range transported conditions, indicating that secondary nitrate aerosols were the major increasing local species that caused high PM2.5 concentration events. Seven sources of pollution could be distinguished using the PMF model on the basis of the characteristics of the species. Industrial emissions, coal combustion/urban waste incineration, and local emissions from diesel/gasoline vehicles were the main sources that contributed to pollution on high PM2.5 concentration event days. In order to reduction of high PM2.5 concentration events, the control of diesel and gasoline vehicle emission is important and should be given priority.
•The mass fractions of NH4+, K+, Cl− and NO3− increased during PM2.5 event days.•Reduction of coal combustion/urban waste incineration emissions should be prioritized.•The control of vehicle emission is important in the locally emitted periods.•Secondary sulfate was mainly dominated by long-range transport.
On the basis of chemical composition measurements and source apportionment, the priority of source reduction in PM2.5 was determined in a developing aerotropolis.
Biomonitoring studies evaluating air quality via airborne element accumulation patterns in lichens typically control variability by focusing on narrow geographic regions and short time windows. Using ...samples of the widespread “rock‐posy” lichen sampled across the Intermountain Region of the United States, we investigate whether accumulation patterns of generic pollution sources are detectable on broad geographic and temporal scales. We develop a novel Bayesian multivariate receptor modeling (BMRM) approach that sharpens detection and discrimination of candidate pollution sources through (i) regularization of source contributions to each sample and (ii) incorporating estimated lichen secondary chemistry as a factor. Through a simulation study, we demonstrate a distinct advantage in shrinking contributions when they are truly sparse, as would be expected with heterogeneous samples from dispersed collection sites. We contrast analyses employing both standard and sparse BMRMs, and positive matrix factorization (PMF). The sparse model better maintains source identity, as specified though informative prior distributions on elemental profiles. We advocate quantitative profile matching, which reveals that PMF primarily captures variations of the baseline profile for lichen secondary chemistry. Both PMF and BMRM results suggest that the most detectable signatures relate to aeolian dust deposition, while spatial patterns hint at sporadic anthropogenic influence.
In this study, positive matrix factorization, multilinear engine 2, and geographic information systems were used to characterize the spatial-temporal patterns of sources for nine heavy metals in the ...surface sediments of the Yangtze River Estuary in different seasons. Results showed that six sources were identified: agricultural pesticide, marine transportation, chemical factory wastewater, metal smelter waste, atmospheric deposition, and agricultural fertilizer. The proportions of sources were similar during the entire year but varied among the seasons. The differences in the proportions of agricultural pesticide between winter and other seasons were greater than 12%. Over 40% of the Cd concentration in most seasons was attributed to atmospheric deposition, while less than 5% in autumn. The impact strength of most sources, except marine transportation and metal smelter waste, decreased from the inner regions to the adjacent sea. The difference in the impact strength of agricultural pesticide was the largest throughout the study area.
•Source apportionment and seasonal variation was analyzed with PMF and ME2.•Contents of metals were higher in autumn and winter than in spring and summer.•Proportions of sources were similar in a year, while varied largely among seasons.
A particulate matter (PM) source apportionment study was carried out in one of the most polluted districts of Tuscany (Italy), close to an old waste incinerator plant. Due to the high PM10 levels, an ...extensive field campaign was supported by the Regional Government to identify the main PM sources and quantify their contributions. PM10 daily samples were collected for one year and analysed by different techniques to obtain a complete chemical characterisation (elements, ions and carbon fractions). Hourly fine (<2.5 μm) and coarse (2.5–10 μm) aerosol samples were collected by a Streaker sampler for a shorter period and hourly elemental concentrations were obtained by PIXE.
Positive Matrix Factorization (PMF) analysis of daily and hourly data allowed the identification of 10 main sources: six anthropogenic (Biomass Burning, Traffic, Secondary Nitrates, Secondary Sulphates, Incinerator, Heavy Oil combustion), two natural (Saharan Dust and Fresh Sea Salt) and two mixed sources (Local Dust and Aged Sea Salt). Biomass burning turned out to be the main source of PM, accounting for 30% of the PM10 mass as annual average, followed by Traffic (18%) and Secondary Nitrates (14%). Emissions from the Incinerator turned out to be only 2% of PM10 mass on average.
PM10 composition and source apportionment have been assessed in a polluted area near a waste incinerator, by PMF analysis on daily and hourly compositional data sets.
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•One year PM10 source apportionment in a polluted area near a waste incinerator.•PMF applied to both daily and hourly aerosol compositional data sets.•Very high PM values in winter, due to atmospheric stability and specific sources.•Biomass burning and secondary nitrate identified as main PM10 sources.•Low contribution of the incinerator emissions to PM10 concentration.
A top-down approach to evaluate high ozone (O3) formation, attributed to different emission sources, is developed for anti-cyclonic conditions in a region of Hong Kong influenced by meso-scale ...circulations. A near-explicit photochemical model coupled with the Master Chemical Mechanism (MCMv3.2) is used to investigate the chemical characteristics in the region. Important features have been enhanced in this model including the photolysis rates, simulated by the National Center for Atmospheric Research (NCAR) Tropospheric Ultraviolet and Visible (TUV) Radiation Model, as well as hourly variation of Volatile Organic Compound (VOC) concentration input from on-site sampling. In general, the combined model gives a reasonably good representation of high O3 levels in the region. The model successfully captured a multi-day O3 event in the autumn of 2010. Source apportionment via Positive Matrix Factorization (PMF) was carried out on the sampled VOC data, to determine the major sources in the region. Based on the outcomes of the PMF source apportionment, a sensitivity analysis using the developed photochemical model was conducted and used to estimate O3 reduction under different source removal regimes. Results indicate that vehicular emissions are the dominant VOC source contributing to O3 formation. This study has demonstrated a potentially efficient secondary pollutants control methodology, using a combined field measurements and modelling approach.
•An observation based photochemical model simulates a multiday O3 event in Hong Kong.•Positive Matrix Factorization of the measured VOC identified local emission sources.•Vehicle emissions and power generation identified as main precursors to O3 formation.
•Biochar prepared from two invasive plants – Spartina alterniflora and water hyacinth.•Cu(II) removal by biochar depends on pyrolysis temperature and environmental pH.•Ca leaching promoted by Cu(II) ...sorption.•Positive matrix factorization (PMF) was employed to statically analyze sorption data.
Alkali and alkaline earth metallic (AAEM) species water leaching and Cu(II) sorption by biochar prepared from two invasive plants, Spartina alterniflora (SA) and water hyacinth (WH), were explored in this work. Significant amounts of Na and K can be released (maximum leaching for Na 59.0mgg−1 and K 79.9mgg−1) from SA and WH biochar when they are exposed to contact with water. Cu(II) removal by biochar is highly related with pyrolysis temperature and environmental pH with 600–700°C and pH of 6 showing best performance (29.4 and 28.2mgg−1 for SA and WH biochar). Cu(II) sorption exerts negligible influence on Na/K/Mg leaching but clearly promotes the release of Ca. Biochars from these two plant species provide multiple benefits, including nutrient release (K), heavy metal immobilization as well as promoting the aggregation of soil particles (Ca) for soil amelioration. AAEM and Cu(II) equilibrium concentrations in sorption were analyzed by positive matrix factorization (PMF) to examine the factors underlying the leaching and sorption behavior of biochar. The identified factors can provide insightful understanding on experimental phenomena.
A mobile laboratory unit (MOBILAB) with on-board instrumentation (Scanning Mobility Particle Sizer, SMPS; Ambient NOx analyzer) was used to measure size-resolved particle number concentrations (PNCs) ...of quasi-ultrafine particles (UFPs, 9–372 nm), along with NOx, in road microenvironments. On-road measurements were carried out in and around a large Greek urban agglomeration, the Thessaloniki Metropolitan Area (TMA). Two 2-week measurement campaigns were conducted during the warm period of 2011 and the cold period of 2012. During each sampling campaign, MOBILAB was driven through a 5-day inner-city route and a second 5-day external route covering in total a wide range of districts (urban, urban background, industrial and residential), and road types (major and minor urban roads, freeways, arterial and interurban roads). All routes were conducted during working days, in morning and in afternoon hours under real-world traffic conditions. Spatial classification of MOBILAB measurements involved the assignment of measurement points to location bins defined by the aspect ratio of adjacent urban street canyons (USCs). Source apportionment was further carried out, by applying Positive Matrix Factorization (PMF) to particle size distribution data. Apportioned PMF factors were interpreted, by employing a two-step methodology, which involved (a) statistical association of PMF factor contributions with 12 h air-mass back-trajectories ending at the TMA during MOBILAB measurements, and (b) Multiple Linear Regression (MLR) using PMF factor contributions as the dependent variables, while relative humidity, solar radiation flux, and vehicle speed were used as the independent variables. The applied data analysis showed that low-speed cruise and high-load engine operation modes are the two dominant sources of UFPs in most of the road microenvironments in the TMA, with significant contributions from background photochemical processes during the warm period, explaining the reversed seasonal variation of UFP concentrations, compared to those observed in cities across Northern Europe. It was also demonstrated that town planning exerts a profound effect on the mitigation of traffic emissions.
•Mobile measurements of UFPs were performed on various road microenvironments.•PMF was applied to particle size distribution data for source apportionment.•Two distinct vehicle exhaust emission patterns are the dominant sources of UFPs.•Background photochemical processes also contribute significantly in the warm period.
► A CFPP located in Rochester, NY was closed over 4-month period in early 2008. ► The ambient Hg concentrations significantly decreased after the CFPP closure. ► PMF results show Hg apportioned to ...the CFPP factor significantly decreased. ► CPF results show the greatest Hg reduction was with winds pointing toward the CFPP. ► These changes were clearly attributable to the closure of the CFPP.
In the spring of 2008, a 260MWe coal-fired power plant (CFPP) located in Rochester, New York was closed over a 4month period. Using a 2-years data record, the impacts of the shutdown of the CFPP on nearby ambient concentrations of three Hg species were quantified. The arithmetic average ambient concentrations of gaseous elemental mercury (GEM), gaseous oxidized mercury (GOM), and particulate mercury (PBM) during December 2007–November 2009 were 1.6ngm−3, 5.1pgm−3, and 8.9pgm−3, respectively. The median concentrations of GEM, GOM, and PBM significantly decreased by 12%, 73%, and 50% after the CFPP closed (Mann–Whitney test, p<0.001). Positive Matrix Factorization (EPA PMF v4.1) identified six factors including O3-rich, traffic, gas phase oxidation, wood combustion, nucleation, and CFPP. When the CFPP was closed, median concentrations of GEM, GOM, and PBM apportioned to the CFPP factor significantly decreased by 25%, 74%, and 67%, respectively, compared to those measured when the CFPP was still in operation (Mann–Whitney test, p<0.001). Conditional probability function (CPF) analysis showed the greatest reduction in all three Hg species was associated with northwesterly winds pointing toward the CFPP. These changes were clearly attributable to the closure of the CFPP.
Some of the challenges to apportioning PAH-related remedy costs at contaminated sediment sites include the lack of source samples, different PAH signatures associated with the same source, historical ...PAH sources long removed, mixing of urban sediment by boat traffic, and, in turn, PAHs mixing and weathering. Unmixing of PAH fingerprints in sediment sites to PAH source classes (petrogenic, pyrogenic, and runoff) is typically a first step to tracking PAH upland sources and ultimately, responsible parties. This work demonstrates using positive matrix factorization (PMF) as a method to unmix PAH fingerprints to its source classes.
A large PAH dataset (over 700 samples) assembled from contaminated urban sediment sites was used as an input to PMF. Using a 3-factor PMF analysis, a petrogenic, pyrogenic, and runoff/weathered PAH end-member fingerprints were identified. Different numerical mixing percentages of the PMF-identified end-member sources were able to replicate the sediment-measured PAH fingerprints, with the percent contribution of each of the end members to each sediment sample calculated. The demonstrated work provides a method to satisfy the unmixing of PAH fingerprints to its source classes, as a step towards apportioning of PAH contamination.
For the purpose of systematically characterizing the ambient volatile organic compound (VOC) levels and their emission sources in the Pearl River Delta (PRD) of China, a grid study with VOC samples ...simultaneously taken at 84 sites over the PRD was conducted in summer and winter of 2008 and 2009. Positive Matrix Factorization (PMF) model was applied to identify the major VOC contributing sources and their temporal and spatial variations. Nine sources were identified, with gasoline exhaust, industrial emission and LPG leakage & propellant emission the top three significant sources. They accounted for 23%, 16% and 13% of the ambient VOC levels, respectively. Control measures should be therefore targeted on mitigating the VOC emissions from the traffic-related and industrial-related sources. The total VOC level did not show strong increase from 5 a.m. to 10 a.m. during all the four sampling campaigns, which may result from stronger wind and higher mixing height at 10 a.m. Three hotspot areas with significant VOC contributions were identified by source apportionment analysis: (1) the Pearl River Estuary; (2) an area from Central Dongguan to North Shenzhen; and (3) the Zhuhai–Zhongshan–Jiangmen area. For better characterizing the roles of VOC and NOx in producing the secondary pollutants and to identify specific sources emitting excessive concentrations of precursors, the emission-oriented Photochemical Assessment Monitoring Station (PAMS) network is recommended to be established in the PRD. Three PAMS networks are suggested in correspondence to the three identified hotspot areas.
► A grid study was initiated with VOC samples simultaneously taken at 84 sites over the PRD. ► PMF model was applied to identify major VOC sources and their spatiotemporal variations. ► Hotspot areas with significant VOC contributions were identified over the PRD. ► Emission-oriented monitoring network, such as PAMS, is recommended to establish in the PRD.
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