Primary Primary Particulate Matter Research Articles

Toxicological evaluation of primary particulate matter emitted from combustion of aviation fuel

Recently, Sustainable Aviation Fuel (SAF) blends and novel combustion technologies have been introduced to reduce aircraft engine emissions. However, there is limited knowledge about the impact of combustion technology and fuel composition on toxicity of primary Particulate Matter (PM) emissions, comparable to regulated non-volatile PM (nvPM).In this study, primary PM was collected on filters using a standardised approach, from both a Rich-Quench-Lean (RQL) combustion rig and a bespoke liquid fuelled Combustion Aerosol Standard (CAST) Generator burning 12 aviation fuels including conventional Jet-A, SAFs, and blends thereof. The fuels varied in aromatics (0–25.2%), sulphur (0–3000 ppm) and hydrogen (13.43–15.31%) contents. Toxicity of the collected primary PM was studied in vitro utilising Air-Liquid Interface (ALI) exposure of lung epithelial cells (Calu-3) in monoculture and co-culture with macrophages (differentiated THP-1 cells). Cells were exposed to PM extracted from filters and nebulised from suspensions using a cloud-based ALI exposure system. Toxicity readout parameters were analysed 24 h after exposure.Results showed presence of genotoxicity and changes in gene expression at dose levels which did not induce cytotoxicity. DNA damage was detected through Comet assay in cells exposed to CAST generated samples. Real-Time PCR performed to investigate the expression profile of genes involved in oxidative stress and DNA repair pathways showed different behaviours after exposure to the various PM samples. No differences were found in pro-inflammatory interleukin-8 secretion. This study indicates that primary PM toxicity is driven by wider factors than fuel composition, highlighting that further work is needed to substantiate the full toxicity of aircraft exhaust PM inclusive of secondary PM emanating from numerous engine technologies across the power range burning conventional Jet-A and SAF.

Satellite-based, top-down approach for the adjustment of aerosol precursor emissions over East Asia: the TROPOspheric Monitoring Instrument (TROPOMI) NO2 product and the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol optical depth (AOD) data fusion product and its proxy

Abstract. In response to the need for an up-to-date emissions inventory and the recent achievement of geostationary observations afforded by the Geostationary Environment Monitoring Spectrometer (GEMS) and its sister instruments, this study aims to establish a top-down approach for adjusting aerosol precursor emissions over East Asia. This study involves a series of the TROPOspheric Monitoring Instrument (TROPOMI) NO2 product, the GEMS aerosol optical depth (AOD) data fusion product and its proxy product, and chemical transport model (CTM)-based inverse modeling techniques. We begin by sequentially adjusting bottom-up estimates of nitrogen oxides (NOx) and primary particulate matter (PM) emissions, both of which significantly contribute to aerosol loadings over East Asia to reduce model biases in AOD simulations during the year 2019. While the model initially underestimates AOD by 50.73 % on average, the sequential emissions adjustments that led to overall increases in the amounts of NOx emissions by 122.79 % and of primary PM emissions by 76.68 % and 114.63 % (single- and multiple-instrument-derived emissions adjustments, respectively) reduce the extents of AOD underestimation to 33.84 % and 19.60 %, respectively. We consider the outperformance of the model using the emissions constrained by the data fusion product to be the result of the improvement in the quantity of available data. Taking advantage of the data fusion product, we perform sequential emissions adjustments during the spring of 2022, the period during which the substantial reductions in anthropogenic emissions took place accompanied by the COVID-19 pandemic lockdowns over highly industrialized and urbanized regions in China. While the model initially overestimates surface PM2.5 concentrations by 47.58 % and 20.60 % in the North China Plain (NCP) region and South Korea (hereafter referred to as Korea), the sequential emissions adjustments that led to overall decreases in NOx and primary PM emissions by 7.84 % and 9.03 %, respectively, substantially reduce the extents of PM2.5 underestimation to 19.58 % and 6.81 %, respectively. These findings indicate that the series of emissions adjustments, supported by the TROPOMI and GEMS-involved data fusion products, performed in this study are generally effective at reducing model biases in simulations of aerosol loading over East Asia; in particular, the model performance tends to improve to a greater extent on the condition that spatiotemporally more continuous and frequent observational references are used to capture variations in bottom-up estimates of emissions. In addition to reconfirming the close association between aerosol precursor emissions and AOD as well as surface PM2.5 concentrations, the findings of this study could provide a useful basis for how to most effectively exploit multisource top-down information for capturing highly varying anthropogenic emissions.

Quantifying international and interstate contributions to primary ambient PM2.5 and PM10 in a complex metropolitan atmosphere

We quantify the contributions of long-range and regionally transported aerosols to ambient primary PM2.5 and PM10 in a representative United States industrialized/urban atmosphere via detailed elemental analysis and chemical mass balance (CMB) modeling after identifying their presence using a variety of publicly available satellite data/information, software products, and synoptic-scale aerosol models. A year-long study in Houston, Texas identified North African dust as the principal long-range global source of primary particulate matter (PM). CMB estimated transatlantic dust from the Sahara-Sahel region to be dominant in the summer months contributing an average of 3.5 μg m−3 to PM2.5 and 7.9 μg m−3 to PM10 during May–August, i.e., the active Saharan dust season. Biomass burning was the chief source of regionally transported PM impacting air quality on different occasions throughout the year depending on the fire location. Four major biomass combustion events affecting air quality in Texas were calculated to contribute an average of 1.3 μg m−3 to PM2.5 and 1.4 μg m−3 to PM10 in corresponding samples whose origins were tracked to Canada, southeastern states of USA, and Central America using fire maps, HYSPLIT back trajectories, and the Navy Aerosol Analysis and Prediction System global aerosol model. Elemental concentrations and signature ratios revealed significant mixing of potassium, rare earth metals, and vanadium from proximal and distal crustal (natural) sources with anthropogenically emitted PM. This demonstrates the need to isolate the non-mineral components of these metals to employ them as tracers for primary PM emitted by biomass burning, petroleum refineries, and oil combustion. Transboundary contributions to primary PM2.5 were 1.5 μg m−3 and 3.1 μg m−3 to PM10 adding 16% to annual average mass concentration of both size fractions demonstrating that local sources were primarily responsible for ambient air quality with non-trivial contributions from international and interstate sources. Rigorously identifying and quantifying aerosol sources assists in improving air quality management policies designed to protect public health and comply with ever-decreasing federal PM standards that allow state agencies to exclude contributions that are not reasonably controllable or preventable from regulatory decisions and actions.

화학 수송 모델과 기상 모델을 이용한 석탄 화력 발전소에서 배출되는 미세먼지의 거동 분석

A coal-fired power plant is considered as one of the main sources for fine particulate matters. Particulate matter generated from the power plant can be classified into the primary particulate matter (PM) and the secondary PM. The primary PM is ejected as a solid-state and the secondary PM is ejected as a gaseous state called a precursor and becomes a solid-state by chemical reactions in the atmosphere. Since the size of the secondary PM is smaller than 2.5 micrometers, it can cause severe health problems. Therefore, accurate prediction of advection and diffusion of PM generated from a coal-fired power plant is highly necessary. In the present study, the WRF-Chem (Weather Research and Forecasting–Chemistry) model is used to simulate air quality near a coal-fired power plant. The Carbon-bond mechanism version Z (CBMZ), is used to predict formation of the secondary PM. An aerosol model, which simulates aerosol interactions and chemistry (MOSAIC), is used to predict the transport of fine particles. The meteorological model WRF is used to predict dispersion of fine particles and gaseous components.

Regional Inequality and Influencing Factors of Primary PM Emissions in the Yangtze River Delta, China

In recent years, haze pollution has become more and more serious in the Yangtze River Delta (YRD). However, the impact mechanism of socio-economic factors on primary particulate matter (PM) emissions remains unclear. Based on the provincial primary PM emission data in the YRD from 1995 to 2014, this paper used Slope, Theil index, and Stochastic Impacts by Regression on Population, Affluence, and Technology (STIAPAT) models to quantitatively identify the regional differences of primary PM emissions and explore the key influencing factors. The results showed that primary fine particulate matter (PM2.5), inhalable particulate (PM10), and total suspended particulate (TSP) emissions all featured an upward trend of fluctuation over the study period. The regional differences in primary TSP emissions in the YRD region was gradually shrinking and the regional differences of primary PM2.5 and PM10 emissions presented a rising trend of fluctuation. The estimated coefficient of population size, energy structure, and fixed assets investment (FAI) were all significantly positive at the level of 1%. The negative effect of economic growth on energy PM emissions was significant under the level of 1%. The increase of foreign direct investment (FDI) had different effects on primary PM2.5, PM10, and TSP emissions. In addition, the influence of energy intensity on primary PM emission from energy consumption are mainly negative but not significant even under the level of 10%. These conclusions have guiding significance for the formulation of PM emission reduction policy without affecting YRD’s economic development.

Scenario Study on PM emission Reduction in Cement Industry

Cement industry is one of the high pollution industries in China. Evaluation of the primary particulate matter (PM) emission status and the reduction potential is not only important for our understanding of the effectiveness of current pollution control measures but also vital for future policy design. In this study, PM emitted from cement producing process in 2014 was calculated using an emission factor method. Three PM emission control scenarios were set up considering source control, process management and end-of-pipe treatment, and the PM emission reduction by 2020 under the three scenarios was predicted, respectively. In 2014, the primary PM emission from cement industry was 1.95 million tons. By 2020, the productions of cement and clinker were expected to increase by 12% and 7%, respectively, and the PM emission would increase by about 10%. By implementation of GB4915-2013 and comprehensive control of fugitive PM emission, the PM emission would probably be reduced by 34%. Another 7% decrease would be expected from source control. The second scenario can be considered as an assessment of the effectiveness of the revised emission standard, and this research can be used as a technical support to the environmental management authorities to make relevant policies.

Use of high-order sensitivity analysis and reduced-form modeling to quantify uncertainty in particulate matter simulations in the presence of uncertain emissions rates: A case study in Houston

Abstract Regional air quality models are widely used to evaluate control strategy effectiveness. As such, it is important to understand the accuracy of model simulations to establish confidence in model performance and to guide further model development. Particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) is regulated as one of the criteria pollutants by the National Ambient Air Quality Standards (NAAQS), and PM2.5 concentrations have a complex dependence on the emissions of a number of precursors, including SO2, NOx, NH3, VOCs, and primary particulate matter (PM). This study quantifies how the emission-associated uncertainties affect modeled PM2.5 concentrations and sensitivities using a reduced-form approach. This approach is computationally efficient compared to the traditional Monte Carlo simulation. The reduced-form model represents the concentration-emission response and is constructed using first- and second-order sensitivities obtained from a single CMAQ/HDDM-PM simulation. A case study is conducted in the Houston-Galveston-Brazoria (HGB) area. The uncertainty of modeled, daily average PM2.5 concentrations due to uncertain emissions is estimated to fall between 42% and 52% for different simulated concentration levels, and the uncertainty is evenly distributed in the modeling domain. Emission-associated uncertainty can account for much of the difference between simulation and ground measurements as 60% of observed PM2.5 concentrations fall within the range of one standard deviation of corresponding simulated PM2.5 concentrations. Uncertainties in meteorological fields as well as the model representation of secondary organic aerosol formation are the other two key contributors to the uncertainty of modeled PM2.5. This study also investigates the uncertainties of the simulated first-order sensitivities, and found that the larger the first-order sensitivity, the lower its uncertainty associated with emissions. Sensitivity of PM2.5 to primary PM has the lowest uncertainty while sensitivity of PM2.5 to VOC has the highest uncertainty associated with emission inputs.

Size-resolved global emission inventory of primary particulate matter from energy-related combustion sources

Current emission inventories provide information about the mass emissions of different chemical species from different emitting sources without information concerning the size distribution of primary particulate matter (PM). The size distribution information, however, is an important input into chemical transport models that determine the fate of PM and its impacts on climate and public health. At present, models usually make rather rudimentary assumptions about the size distribution of primary PM emissions in their model inputs. In this study, we develop a global and regional, size-resolved, mass emission inventory of primary PM emissions from source-specific combustion components of the residential, industrial, power, and transportation sectors for the year 2010. Uncertainties in the emission profiles are also provided. The global size-resolved PM emissions show a distribution with a single peak and the majority of the mass of particles in size ranges smaller than 1 μm. The PM size distributions for different sectors and world regions vary considerably, due to the different combustion characteristics. Typically, the sizes of particles decrease in the order: power sector > industrial sector > residential sector > transportation sector. Three emission scenarios are applied to the baseline distributions to study the likely changes in size distribution of emissions as clean technologies are implemented.

Sources and contents of air pollution affecting term low birth weight in Los Angeles County, California, 2001–2008

BackgroundLow birth weight (LBW, <2500g) has been associated with exposure to air pollution, but it is still unclear which sources or components of air pollution might be in play. The association between ultrafine particles and LBW has never been studied. ObjectivesTo study the relationships between LBW in term born infants and exposure to particles by size fraction, source and chemical composition, and complementary components of air pollution in Los Angeles County (California, USA) over the period 2001–2008. MethodsBirth certificates (n=960,945) were geocoded to maternal residence. Primary particulate matter (PM) concentrations by source and composition were modeled. Measured fine PM, nitrogen dioxide and ozone concentrations were interpolated using empirical Bayesian kriging. Traffic indices were estimated. Associations between LBW and air pollution metrics were examined using generalized additive models, adjusting for maternal age, parity, race/ethnicity, education, neighborhood income, gestational age and infant sex. ResultsIncreased LBW risks were associated with the mass of primary fine and ultrafine PM, with several major sources (especially gasoline, wood burning and commercial meat cooking) of primary PM, and chemical species in primary PM (elemental and organic carbon, potassium, iron, chromium, nickel, and titanium but not lead or arsenic). Increased LBW risks were also associated with total fine PM mass, nitrogen dioxide and local traffic indices (especially within 50m from home), but not with ozone. Stronger associations were observed in infants born to women with low socioeconomic status, chronic hypertension, diabetes and a high body mass index. ConclusionsThis study supports previously reported associations between traffic-related pollutants and LBW and suggests other pollution sources and components, including ultrafine particles, as possible risk factors.

Secondary organic aerosol production from diesel vehicle exhaust: impact of aftertreatment, fuel chemistry and driving cycle

Abstract. Environmental chamber ("smog chamber") experiments were conducted to investigate secondary organic aerosol (SOA) production from dilute emissions from two medium-duty diesel vehicles (MDDVs) and three heavy-duty diesel vehicles (HDDVs) under urban-like conditions. Some of the vehicles were equipped with emission control aftertreatment devices, including diesel particulate filters (DPFs), selective catalytic reduction (SCR) and diesel oxidation catalysts (DOCs). Experiments were also performed with different fuels (100% biodiesel and low-, medium- or high-aromatic ultralow sulfur diesel) and driving cycles (Unified Cycle,~Urban Dynamometer Driving Schedule, and creep + idle). During normal operation, vehicles with a catalyzed DPF emitted very little primary particulate matter (PM). Furthermore, photooxidation of dilute emissions from these vehicles produced essentially no SOA (below detection limit). However, significant primary PM emissions and SOA production were measured during active DPF regeneration experiments. Nevertheless, under reasonable assumptions about DPF regeneration frequency, the contribution of regeneration emissions to the total vehicle emissions is negligible, reducing PM trapping efficiency by less than 2%. Therefore, catalyzed DPFs appear to be very effective in reducing both primary PM emissions and SOA production from diesel vehicles. For both MDDVs and HDDVs without aftertreatment substantial SOA formed in the smog chamber – with the emissions from some vehicles generating twice as much SOA as primary organic aerosol after 3 h of oxidation at typical urban VOC / NOx ratios (3 : 1). Comprehensive organic gas speciation was performed on these emissions, but less than half of the measured SOA could be explained by traditional (speciated) SOA precursors. The remainder presumably originates from the large fraction (~30%) of the nonmethane organic gas emissions that could not be speciated using traditional one-dimensional gas chromatography. The unspeciated organics – likely comprising less volatile species such as intermediate volatility organic compounds – appear to be important SOA precursors; we estimate that the effective SOA yield (defined as the ratio of SOA mass to reacted precursor mass) was 9 ± 6% if both speciated SOA precursors and unspeciated organics are included in the analysis. SOA production from creep + idle operation was 3–4 times larger than SOA production from the same vehicle operated over the Urban Dynamometer Driving Schedule (UDDS). Fuel properties had little or no effect on primary PM emissions or SOA formation.

Secondary organic aerosol formation exceeds primary particulate matter emissions for light-duty gasoline vehicles

Abstract. The effects of photochemical aging on emissions from 15 light-duty gasoline vehicles were investigated using a smog chamber to probe the critical link between the tailpipe and ambient atmosphere. The vehicles were recruited from the California in-use fleet; they represent a wide range of model years (1987 to 2011), vehicle types and emission control technologies. Each vehicle was tested on a chassis dynamometer using the unified cycle. Dilute emissions were sampled into a portable smog chamber and then photochemically aged under urban-like conditions. For every vehicle, substantial secondary organic aerosol (SOA) formation occurred during cold-start tests, with the emissions from some vehicles generating as much as 6 times the amount of SOA as primary particulate matter (PM) after 3 h of oxidation inside the chamber at typical atmospheric oxidant levels (and 5 times the amount of SOA as primary PM after 5 × 106 molecules cm−3 h of OH exposure). Therefore, the contribution of light-duty gasoline vehicle exhaust to ambient PM levels is likely dominated by secondary PM production (SOA and nitrate). Emissions from hot-start tests formed about a factor of 3–7 less SOA than cold-start tests. Therefore, catalyst warm-up appears to be an important factor in controlling SOA precursor emissions. The mass of SOA generated by photooxidizing exhaust from newer (LEV2) vehicles was a factor of 3 lower than that formed from exhaust emitted by older (pre-LEV) vehicles, despite much larger reductions (a factor of 11–15) in nonmethane organic gas emissions. These data suggest that a complex and nonlinear relationship exists between organic gas emissions and SOA formation, which is not surprising since SOA precursors are only one component of the exhaust. Except for the oldest (pre-LEV) vehicles, the SOA production could not be fully explained by the measured oxidation of speciated (traditional) SOA precursors. Over the timescale of these experiments, the mixture of organic vapors emitted by newer vehicles appears to be more efficient (higher yielding) in producing SOA than the emissions from older vehicles. About 30% of the nonmethane organic gas emissions from the newer (LEV1 and LEV2) vehicles could not be speciated, and the majority of the SOA formed from these vehicles appears to be associated with these unspeciated organics. By comparing this study with a companion study of diesel trucks, we conclude that both primary PM emissions and SOA production for light-duty gasoline vehicles are much greater than for late-model (2007 and later) on-road heavy-duty diesel trucks.

Identifying PM2.5 and PM0.1 Sources for Epidemiological Studies in California

The University of California-Davis_Primary (UCD_P) model was applied to simultaneously track ∼ 900 source contributions to primary particulate matter (PM) in California for seven continuous years (January 1st, 2000 to December 31st, 2006). Predicted source contributions to primary PM2.5 mass, PM1.8 elemental carbon (EC), PM1.8 organic carbon (OC), PM0.1 EC, and PM0.1 OC were in general agreement with the results from previous source apportionment studies using receptor-based techniques. All sources were further subjected to a constraint check based on model performance for PM trace elemental composition. A total of 151 PM2.5 sources and 71 PM0.1 sources contained PM elements that were predicted at concentrations in general agreement with measured values at nearby monitoring sites. Significant spatial heterogeneity was predicted among the 151 PM2.5 and 71 PM0.1 source concentrations, and significantly different seasonal profiles were predicted for PM2.5 and PM0.1 in central California vs southern California. Population-weighted concentrations of PM emitted from various sources calculated using the UCD_P model spatial information differed from the central monitor estimates by up to 77% for primary PM2.5 mass and 148% for PM2.5 EC because the central monitor concentration is not representative of exposure for nearby population. The results from the UCD_P model provide enhanced source apportionment information for epidemiological studies to examine the relationship between health effects and concentrations of primary PM from individual sources.

Particulate Matter and Organic Vapor Emissions from a Helicopter Engine Operating on Petroleum and Fischer–Tropsch Fuels

Particle and gaseous emissions from a T63 gas-turbine engine were characterized using three fuels: standard military jet fuel (JP-8), Fischer–Tropsch (FT) synthetic fuel, and a 50:50 blend of each. Primary emissions were sampled using a dilution tunnel and sampling trains with both filters and sorbent tubes. Primary particulate matter (PM) and gaseous emissions for the neat FT and blend fuels were reduced relative to emissions when using JP-8 fuel at both idle and cruise loads. At idle load, PM mass emissions are reduced by 65% with neat FT fuel and by 50% for the 50:50 blend compared to neat JP-8 fuel. The JP-8/FT blend thus decreases emissions beyond the linear average of the emissions for the individual fuels. At idle load, FT fuel reduced total hydrocarbon emissions by 20%, while the blend showed no significant change compared to neat JP-8. At cruise load, neat FT fuel resulted in an 80% reduction in primary PM emissions and a 30% reduction in total hydrocarbon emissions compared to neat JP-8. Decreas...

Size-resolved source apportionment of ambient particles by positive matrix factorization at Gosan background site in East Asia

Abstract. Size- and time-resolved aerosol samples were collected using an eight-stage Davis rotating unit for monitoring (DRUM) sampler from 29 March to 29 May in 2002 at Gosan, Jeju Island, Korea, which is one of the representative background sites in East Asia. These samples were analyzed using synchrotron X-ray fluorescence for 3-h average concentrations of 19 elements consisting of S, Si, Al, Fe, Ca, Cl, Cu, Zn, Ti, K, Mn, Pb, Ni, V, Se, As, Rb, Cr, Br. The size-resolved data sets were then analyzed using the positive matrix factorization (PMF) technique in order to identify possible sources and estimate their contribution to particulate matter mass. PMF analysis uses the uncertainty of the measured data to provide an optimal weighting. Fifteen sources were resolved in eight size ranges (0.07~12 μm) and included continental soil, local soil, sea salt, biomass/biofuel burning, coal combustion, oil heating furnace, residual oil fired boiler, municipal incineration, nonferrous metal source, ferrous metal source, gasoline vehicle, diesel vehicle, copper smelter and volcanic emission. PMF analysis of size-resolved source contributions showed that natural sources represented by local soil, sea salt and continental soil contributed about 79% to the predicted primary particulate matter (PM) mass in the coarse size range (1.15~12 μm). On the other hand, anthropogenic sources such as coal combustion and biomass/biofuel burning contributed about 60% in the fine size range (0.56~2.5 μm). The diesel vehicle source contributed the most in the ultra-fine size range (0.07~0.56 μm) and was responsible for about 52% of the primary PM mass.

A module to calculate primary particulate matter emissions and abatement measures in Europe

Primary particulate matter is emitted directly into the atmosphere from various anthropogenic and natural sources such as power plants (combustion of fossil fuels) or forest fires. Secondary particles are formed by transformation of SO2, NOx, NH3, and VOC in the atmosphere. They both contribute to ambient particulate matter concentrations, which may have adverse effects on human health. Health hazards are caused by small particulate size, high number of especially fine (< 2.5 µm) and ultra-fine (< 0.1 µm) particles and/or their chemical composition. As part of an integrated assessment model developed at IIASA, a module on primary particulate matter (PM) emissions has been added to the existing SO2, NOx, NH3 and VOC sections. The module considers so far primary emissions of total suspended particles (TSP), PM10 and PM2.5 from aggregated stationary and mobile sources. A primary PM emission database has been established. Country specific emission factors for stationary sources have been calculated within the module using the ash content of solid fuels.