Search

Abstract Nitrated phenols (NPs) are regarded as a major component of brown carbon and affect the earth environment. Previous studies have suggested that NPs in the atmosphere partially originate from coal combustion; however, the direct emissions have not been validated. In this study, the emissions of ten NPs from ten kinds of coal for residential use were investigated via laboratory combustion experiments in which PM 2.5 samples were collected and analyzed by UHPLC-MS. The fine particulate NPs emitted from coal combustion were dominated by nitrocatechols and the methyl substituents under most conditions, while the emission profile of nitrated phenols varies with different rank of coals. Based on the emission ratios of NPs/PM 2.5 obtained from this study and the PM 2.5 emission factors in the literature, the emission factors of fine particulate NPs for chunks of lignite, bituminite, and anthracite and for briquettes of anthracite were calculated as 0.2–10.1 mg kg−1. According to the residential coal consumption in 30 provinces in China in 2016, the total emission of fine particulate NPs from the residential coal combustion was estimated to be 178 ± 42 Mg. Graphical abstract Image 1 Highlights • Emission profile of nitrated phenols varies with coal maturity and combustion stage. • Emission factor of nitrated phenols decreases with the increasing rank of coal. • Emission of nitrated phenols from residential coal combustion in China was estimated. [ABSTRACT FROM AUTHOR]

Norway spruce ( Picea abies ) is one of the dominant tree species in the European boreal zone with the capacity to grow over large areas within Europe. It is an important emitter of biogenic volatile organic compounds (BVOCs), which can act as precursors of photochemical smog and ozone and contribute to the formation and growth of secondary organic aerosols (SOA) in the atmosphere. Isoprenoid emissions were measured from Norway spruce trees at seven different sites, distributed from Ljubljana in Slovenia to Piikkiö in Finland. Four of the sites were part of a network of genetically identical spruce trees and contained two separate provenances. The remaining three sites were part of other networks which have been used to conduct studies in the European boreal zone. There were minimal differences in the standardized emission rates between sites and across latitudes. The emission profile differed between provenances and sites, but there were not any distinct patterns which could be connected to a change in latitude. By using genetically identical trees and comparing the emission rates between sites and with genetically different trees, it was observed that the emission patterns were mostly influenced by genetics. But in order to confirm this possible stability of the relative emission profile based on genetics, more studies need to be performed. The effects of branch height, season and variation between years on observed emission pattern variations were also investigated. There were indications of potential influences of all three factors. However, due to different experimental setups between measurement campaigns, it is difficult to draw any robust conclusions. [ABSTRACT FROM AUTHOR]

Atmospheric PM pollution from traffic comprises not only direct emissions but also non-exhaust emissions because resuspension of road dust that can produce high human exposure to heavy metals, metalloids, and mineral matter. A key task for establishing mitigation or preventive measures is estimating the contribution of road dust resuspension to the atmospheric PM mixture. Several source apportionment studies, applying receptor modeling at urban background sites, have shown the difficulty in identifying a road dust source separately from other mineral sources or vehicular exhausts. The Multilinear Engine (ME-2) is a computer program that can solve the Positive Matrix Factorization (PMF) problem. ME-2 uses a programming language permitting the solution to be guided toward some possible targets that can be derived from a priori knowledge of sources (chemical profile, ratios, etc.). This feature makes it especially suitable for source apportionment studies where partial knowledge of the sources is available. In the present study ME-2 was applied to data from an urban background site of Barcelona (Spain) to quantify the contribution of road dust resuspension to PM10 and PM2.5 concentrations. Given that recently the emission profile of local resuspended road dust was obtained (Amato, F., Pandolfi, M., Viana, M., Querol, X., Alastuey, A., Moreno, T., 2009. Spatial and chemical patterns of PM10 in road dust deposited in urban environment. Atmospheric Environment 43 (9), 1650–1659), such a priori information was introduced in the model as auxiliary terms of the object function to be minimized by the implementation of the so-called “pulling equations”. ME-2 permitted to enhance the basic PMF solution (obtained by PMF2) identifying, beside the seven sources of PMF2, the road dust source which accounted for 6.9 μg m−3 (17%) in PM10, 2.2 μg m−3 (8%) of PM2.5 and 0.3 μg m−3 (2%) of PM1. This reveals that resuspension was responsible of the 37%, 15% and 3% of total traffic emissions respectively in PM10, PM2.5 and PM1. Therefore the overall traffic contribution resulted in 18 μg m−3 (46%) in PM10, 14 μg m−3 (51%) in PM2.5 and 8 μg m−3 (48%) in PM1. In PMF2 this mass explained by road dust resuspension was redistributed among the rest of sources, increasing mostly the mineral, secondary nitrate and aged sea salt contributions. [Copyright &y& Elsevier]

Receptor modelling techniques are used to identify and quantify the contributions from emission sources to the levels and major and trace components of ambient particulate matter (PM). A wide variety of receptor models are currently available, and consequently the comparability between models should be evaluated if source apportionment data are to be used as input in health effects studies or mitigation plans. Three of the most widespread receptor models (principal component analysis, PCA; positive matrix factorization, PMF; chemical mass balance, CMB) were applied to a single PM10 data set (n=328 samples, 2002–2005) obtained from an industrial area in NE Spain, dedicated to ceramic production. Sensitivity and temporal trend analyses (using the Mann–Kendall test) were applied. Results evidenced the good overall performance of the three models (r 2>0.83 and α>0.91×between modelled and measured PM10 mass), with a good agreement regarding source identification and high correlations between input (CMB) and output (PCA, PMF) source profiles. Larger differences were obtained regarding the quantification of source contributions (up to a factor of 4 in some cases). The combined application of different types of receptor models would solve the limitations of each of the models, by constructing a more robust solution based on their strengths. The authors suggest the combined use of factor analysis techniques (PCA, PMF) to identify and interpret emission sources, and to obtain a first quantification of their contributions to the PM mass, and the subsequent application of CMB. Further research is needed to ensure that source apportionment methods are robust enough for application to PM health effects assessments. [Copyright &y& Elsevier]

Abstract: This paper presents a fine particle emission profile for a large metallurgical coke production facility. The profile is developed from highly time-resolved, ambient air quality measurements made at a fence line site adjacent to the plant. A fence line approach was employed because the coke plant has hundreds of stacks and other emission points, making it difficult to develop an integrated, facility-wide emission profile using stack sampling techniques. Continuous or semi-continuous measurements of PM2.5 mass, PM10 mass, SO2, NO x , organic and elemental carbon (OC and EC), particle size and number, 11 trace metals, wind direction and wind speed were made. Background pollutant levels were also measured. A combination of highly time-resolved meteorology and air quality data were used to determine when the coke facility emissions influenced the sampling site. Concentrations for most pollutants at the fence line site were one to two orders of magnitude higher than background levels when the facility plume heavily influenced the fence line site. Highly time-resolved measurements are essential to resolve these relatively short-duration, large spikes in pollutant concentrations. Simply measuring wind direction is insufficient. From these highly time-resolved measurements an average PM2.5 emission profile for the coke facility was developed. The profile is dominated by OC (40%±9% of PM2.5 mass emissions) and EC (25%±5% of PM2.5 mass emissions). Significant contributions of certain trace metals were also observed, including As, Zn, Se, and Pb. The particle emissions are dominated by the fine fraction, with PM2.5 estimated to contribute 84%±14% of the PM10 mass. [Copyright &y& Elsevier]

Abstract: The emissions of polycyclic aromatic hydrocarbons (PAHs) were quantified for two joss paper furnaces burning two kinds of joss papers (recycled paper made and virgin bamboo made). A cyclone and a wet scrubber were installed in series on one of the two furnaces. Particulate and gaseous PAHs were collected with a sampling system meeting the criteria of U.S. EPA Modified Method 5. Twenty-one species of PAH were analyzed by GC/MS. Individual PAH emission factors vary from less than 1mgkg−1 fuel to several tens of mgkg−1 fuel. The total (sum of 21 compounds) and the carcinogenic PAH (benz[a]anthracene, chrysene, benzo[b]fluoranthene, benzo[k]fluoranthene, benzo[a]pyrene, indeno[1,2,3,-cd]pyrene, dibenz[a,h]anthracene) emission factors were not statistically different for the two furnaces and averaged 71.0 and 3.2mgkg−1, respectively. The PAH profiles showed a predominance of naphthalene (58.1%), phenanthrene (11.7%) and fluorene (7.5%). Of the two joss papers examined, bamboo-made joss paper showed less emission in both particulate and gaseous PAHs. For particulate and gaseous PAHs, the removal efficiencies of total PAHs by the air pollution control devices were 42.5% and 11.7%, respectively. PAH emission factors in high airflow conditions were generally lower than those in low airflow condition. [Copyright &y& Elsevier]