Sources Of Particulate Matter Pollution Research Articles

Efficient calibration of cost-efficient particulate matter sensors using machine learning and time-series alignment

Atmospheric particulate matter (PM) poses a significant threat to human health, infiltrating the lungs and brain and leading to severe issues such as heart and lung diseases, cancer, and premature death. The main sources of PM pollution are vehicular and industrial emissions, construction and agricultural activities, and natural phenomena such as wildfires. Research underscores the absence of a safe threshold for particulate exposure, highlighting the crucial need for monitoring PM levels to develop and implement effective risk mitigation measures. Notwithstanding, accurate measurement of PM concentration relies on expensive and cumbersome equipment. Despite the rising popularity of low-cost alternatives, their reliability remains questionable, given their sensitivity to environmental conditions, inherent instability, and manufacturing imperfections. This article proposes a novel approach to efficient correction of low-cost PM sensors. The primary calibration model is a feedforward artificial neural network (ANN), which directly renders predicted output of the corrected sensor based on environmental variables such as temperature, humidity, and atmospheric pressure. The ANN hyper-parameters are identified by aligning time series of prior reference and low-cost sensor readings, which enables the network to learn typical temporal changes of the sensor outcome as a function of the aforementioned parameters as well as operational relationships between the sensor and the reference device. The architecture of the ANN is optimized in terms of the number of neurons in each layer to enhance its generalization capability. Our methodology has been demonstrated using a custom-designed portable monitoring platform and reference data acquired from public stations in Gdansk. The results are indicative of excellent calibration reliability. The achieved correlation coefficients w.r.t. the reference readings are 0.86, 0.88, and 0.72 for PM1, PM10, and PM2.5, respectively, whereas RMSE values are only 3.0, 3.9, and 5.4 µg/m³.

Fine particle pollution during megafires contains potentially toxic elements

Wildfires that raged across Australia during the 2019–2020 ‘Black Summer’ produced an enormous quantity of particulate matter (PM) pollution, with plumes that cloaked many urban centres and ecosystems along the eastern seaboard. This has motivated a need to understand the magnitude and nature of PM exposure, so that its impact on both built and natural environments can be more accurately assessed. Here we present the potentially toxic fingerprint of PM captured by building heating, ventilation, and air conditioning filters in Sydney, Australia during the peak of the Wildfires, and from ambient urban emissions one year later (Reference period). Atmospheric PM and meteorological monitoring data were also assessed to determine the magnitude and source of high PM exposure. The wildfires were a major source of PM pollution in Sydney, exceeding the national standards on 19 % of days between November–February. Wildfire particles were finer and more spherical compared to Reference PM, with count median diameters of 892.1 ± 23.1 versus 1484.8 ± 96.7 nm (mean ± standard error). On an equal-mass basis, differences in potentially toxic elements were predominantly due to higher SO42−-S (median 20.4 vs 4.7 mg g−1) and NO3−-N (2.4 vs 1.2 mg g−1) in Wildfire PM, and higher PO43−-P (10.4 vs 1.4 mg g−1) in Reference PM. Concentrations of remaining elements were similar or lower than Reference PM, except for enrichments to F−, Cl−, dissolved Mn, and particulate Mn, Co and Sb. Fractional solubilities of trace elements were similar or lower than Reference PM, except for enhanced Hg (12.1 vs 1.0 %) and greater variability in Cd, Hg and Mn solubility, which displayed upper quartiles exceeding that of Reference PM. These findings contribute to our understanding of human and ecosystem exposures to the toxic components of mixed smoke plumes, especially in regions downwind of the source.

Source apportionment of ambient PM2.5 in an industrialized city using dispersion-normalized, multi-time resolution factor analyses

Ambient fine particulate matter (PM2.5) data were collected in the lower City of Hamilton, Ontario to apportion the sources of this pollutant over an 18-month period. Hamilton has complex topographical features that may result in worsened air pollution within the lower city, thus, dispersion-normalized, multi-time resolution factor analysis (DN-MT-FA) was used to identify and quantify contributions of factors in a manner that reduced the influence of local meteorology. These factors were secondary organic aerosols type 1 (SOA_1), particulate nitrate (pNO3), particulate sulphate (pSO4), primary traffic organic matter (PTOM), Steel/metal processing and vehicular road dust emissions (Steel & Mobile) and, secondary organic aerosols type 2 (SOA_2) with origins ranging from mainly regional to mainly local. Factors that were mainly local (PTOM, Steel & Mobile, SOA_2) contributed up to 17% of the average PM2.5 mass while mixed local/regional factors (pNO3, pSO4) made up 43% on average, indicating the potential for further reduction of harmful PM concentrations locally. Of particular interest from a health protection perspective, was the composition of PM2.5 on days when an exceedance of the 24-hr WHO air quality guideline for this pollutant was observed. In general, SOA_1 was found to drive summer exceedances while pNO3 dominated in the winter. During the summer period, SOA_1 was attributable to wildfires in the northern parts of Canada while local traffic sources in winter contributed to the high levels of pNO3. While local, industrial factors only had minor relative mass contributions during exceedances, they are high in highly oxidized organic species (SOA_2) and toxic metals (Steel & Mobile). Thus, they are likely to have more impacts on human health. The methods and results described in this work will be useful in understanding prevalent sources of particulate matter pollution in the ambient air in the presence of complex topography and meteorological effects.

Importance of Punctual Monitoring to Evaluate the Health Effects of Airborne Particulate Matter.

Particulate matter (PM) pollution is one of the major public health problems worldwide, given the high mortality attributable to exposure to PM pollution and the high pathogenicity that is found above all in the respiratory, cardiovascular, and neurological systems. The main sources of PM pollution are the daily use of fuels (wood, coal, organic residues) in appliances without emissions abatement systems, industrial emissions, and vehicular traffic. This review aims to investigate the causes of PM pollution and classify the different types of dust based on their size. The health effects of exposure to PM will also be discussed. Particular attention is paid to the measurement method, which is unsuitable in the risk assessment process, as the evaluation of the average PM compared to the evaluation of PM with punctual monitoring significantly underestimates the health risk induced by the achievement of high PM values, even for limited periods of time.

Sources and health effects of fine and ultrafine aerosol particles in an urban environment

The implementation of a liveable urban environment is a challenge for decision makers since cities are typically more and more heavily congested with traffic. The aims of the current study were to characterize relevant sources of particulate matter pollution in an urban greenbelt area in a city of traditional European urban form (Budapest, Hungary) and to evaluate the health effects related to each source. A ten-day-long intensive, wintertime measurement campaign was performed monitoring the particle mass concentration, the size distribution of fine and ultrafine aerosols, the black carbon concentration and the chemical composition of size fractionated particulate matter. An aethalometer model and positive matrix factorization were used to identify and quantify relevant sources. Three of six sources were linked to combustion processes, of which one was identified as fresh traffic emissions and a second as traffic related secondary aerosols (35 nm and 15 nm mode particles, respectively). Biomass combustion originating particles could be identified, with approximately 100 nm size mode and containing elements in the ultrafine size range such as K, Cu, Zn, Br, Pb. Simulations using a Stochastic Lung Model were carried out to study the deposition profiles of particles related to each identified source in the human air passages. Traffic was found to be the most harmful pollution source, and was responsible for 31% of the total number of submicron particles during the wintertime period of the study.

Particulate emissions of heavy duty diesel engines measured from the tailpipe and the dilution tunnel

In China particulate matter (PM) is the leading environmental risk factor to morbidity and mortality, with transport being the main source of PM pollution in cities. China VI emission limit standards, which are one of the most stringent worldwide, have only recently been introduced and there is lack of studies assessing the PM emissions of new engines. In Europe, there are discussions ongoing to permit sampling directly from the tailpipe with fixed dilution for determining the solid particle number (SPN) emissions during type approval of heavy duty engines; something that is practically allowed for on-road testing. In this study the particulate emissions of seven engines were measured. Three of them were fulfilling the China VI limits, two China V, while two of them were China III engines retrofitted with diesel particulate filters (DPFs). One system with evaporation tube was installed at the full dilution tunnel, as prescribed in the regulation, and another one with catalytic stripper was sampling from the tailpipe, both measuring >23 nm and >10 nm particles. The results showed a wide range of particulate emission levels depending on the engine, the existence of a DPF, the connection point of the crankcase ventilation, and the occurrence of active or passive regeneration. The system at the tailpipe measured on average 20% lower than the system at the dilution tunnel. The system at the dilution tunnel measured much higher during one regeneration due to re-nucleation of volatiles downstream of the evaporation tube. For one engine, the emissions of the evaporation tube system were more than double when the crankcase ventilation was connected downstream of the aftertreatment devices. The main message from this study is that a catalytic stripper is necessary for sub-23 nm measurements and tailpipe sampling with fixed dilution is a plausible option.

Small Scale Experiments of PM10 Dispersion Around Obstacles

Particulate matter (PM) is the main determinant of air pollution caused by a variety of natural and human-caused sources. Because it can be suspended in the atmosphere for long periods of time and travel long distances, it can cause a major health crisis for humans and damage the environment as well. Studies are still required to understand how the PM moves around obstacles, especially in urban areas. In this study, small scale experiments were carried out to look into the effects of simple obstacles, heights and distance from the source on the PM10 concentration. Results show that when obstacle heights and distance from the source increase, the PM10 average concentration decrease. Also, turbulence created by the obstacles affects the PM10 concentration in both sensors before and after the obstacle, mainly in cases of high wind speed. In addition, the use of incense sticks as a source of PM pollution illustrated that moderate burning of incense sticks in indoor places could skyrocket the PM10 concentration to an unhealthy level.

Characterizing temporal variability of PM2.5/PM10 ratio and its relationship with meteorological parameters in Bahrain

The Kingdom of Bahrain is ranked in the top 10 most urban ambient air polluted countries in the world due to high particulate matter (PM) concentrations. The main objective of this article is to analyze comprehensively long-term temporal (annual, seasonal, and monthly) trends of PM2.5/PM10 ratios using PM data collected simultaneously from five governorates in Bahrain over six years (2006–2012) by employing state-of-the-art Theil-Sen approach. Results showed the calculated mean PM2.5/PM10 ratio was 0.31 suggesting coarse particulates are the dominant source of PM pollution. Both observed and adjusted temporal trend of PM2.5/PM10 ratios showed significant decrease during the study period. Mean trend analysis of PM2.5/PM10 ratio also showed that it has decreased in all seasons. Annual cycle of PM2.5/PM10 ratio showed that the lowest ratio was observed in April while the highest ratio was witnessed in September. Weekly and diurnal trends of PM2.5/PM10 ratio were investigated using hourly data of PM10 and PM2.5 recorded in 2012. Temporal variation of PM2.5/PM10 ratio did not exhibit any distinctive trend on weekly and diurnal analysis. Correlation analysis showed positive association between PM2.5 and PM10 (r = 0.77) and PM2.5/PM10 ratio exhibited negative association with temperature and wind speed suggesting PM10 concentrations increase in high ambient temperatures and windy days. Bivariate polar plot analysis of PM data together with meteorological parameters demonstrated that sources of PM are primarily located in the west and north-west direction thus identifying surrounding deserts as major contributors to PM pollution.

Understanding the Influence of Crop Residue Burning on PM2.5 and PM10 Concentrations in China from 2013 to 2017 Using MODIS Data.

In recent years, particulate matter (PM) pollution has increasingly affected public life and health. Therefore, crop residue burning, as a significant source of PM pollution in China, should be effectively controlled. This study attempts to understand variations and characteristics of PM10 and PM2.5 concentrations and discuss correlations between the variation of PM concentrations and crop residue burning using ground observation and Moderate Resolution Imaging Spectroradiometer (MODIS) data. The results revealed that the overall PM concentration in China from 2013 to 2017 was in a downward tendency with regional variations. Correlation analysis demonstrated that the PM10 concentration was more closely related to crop residue burning than the PM2.5 concentration. From a spatial perspective, the strongest correlation between PM concentration and crop residue burning existed in Northeast China (NEC). From a temporal perspective, the strongest correlation usually appeared in autumn for most regions. The total amount of crop residue burning spots in autumn was relatively large, and NEC was the region with the most intense crop residue burning in China. We compared the correlation between PM concentrations and crop residue burning at inter-annual and seasonal scales, and during burning-concentrated periods. We found that correlations between PM concentrations and crop residue burning increased significantly with the narrowing temporal scales and was the strongest during burning-concentrated periods, indicating that intense crop residue burning leads to instant deterioration of PM concentrations. The methodology and findings from this study provide meaningful reference for better understanding the influence of crop residue burning on PM pollution across China.

Characterization of Fine Particulate Matter in Sharjah, United Arab Emirates Using Complementary Experimental Techniques

Airborne particulate matter (PM) pollutants were sampled from an urban background site in Sharjah, United Arab Emirates. The fine fraction (PM2.5) (particulates with aerodynamic diameters of less than 2.5 μm) was collected on 47-mm Teflon filters and analyzed using a combined set of non-destructive techniques in order to provide better understanding of the sources of pollutants and their interaction during transport in the atmosphere. These techniques included gravimetric analysis, equivalent black carbon (EBC), X-ray fluorescence, scanning electron microscopy, and X-ray diffraction. Generally, the PM2.5 concentrations are within the limits set by the World Health Organization (WHO) and the United States (US) Environmental Protection Agency. The EBC content is in the range of 10–12% of the total PM concentration (2–4 µg m−3), while S (as ammonium sulfate), Ca (as calcite, gypsum, and calcium carbonate), Si (as quartz), Fe, and Al were the major sources of PM pollution. EBC, ammonium sulfate, Zn, V, and Mn originate from anthropogenic sources such as fossil fuel burning, traffic, and industrial emissions. Natural elements such as Ca, Fe, Al, Si, and Ti are due to natural sources such as crustal materials (enhanced during dust episodes) and sea salts. The average contribution of natural sources in the total PM2.5 mass concentration over the sampling period is about 40%, and the contribution of the secondary inorganic compounds is about 27% (mainly ammonium sulfate in our case). The remaining 22% is assumed to be secondary organic compounds.

Source apportionments of PM2.5 organic carbon during the elevated pollution episodes in the Ordos region, Inner Mongolia, China.

The Ordos region in the southwestern part of Inner Mongolia experiences frequent PM concentrations in excess of the national PM2.5 air quality standards. In order to determine the key sources of PM2.5 contributing to these pollution episodes, the main sources of PM2.5 OC during elevated PM episodes in the Inner Mongolia were analyzed and compared with non-polluted days. This will provide insight to the main sources of particulate matter pollution during the high-pollution episodes and the effective seasonal strategies to control sources of particulate matter during months and with the highest PM concentrations that need to be controlled. The PMF source contributions to OC demonstrated that the industrial/coal combustion (4762.77 ± 1061.54 versus 2726.49 ± 469.75ng/m3; p < 0.001) and mobile source factors (4651.14 ± 681.82 versus 2605.55 ± 276.50ng/m3; p value < 0.001) showed greater contributions to the elevated concentrations during the episode. The spatial analysis of secondary organic carbon (SOC) factors, regional biomass burning, and biogenic sources did not show significant difference in the pollution episodes and the non-polluted months. In addition, the bivariate polar plots and CWT maps of the industrial/coal combustion and mobile illustrated a regional long-range transport patterns from the external sources to the study area, however, adjacent areas were mostly controlling the contributions of these factors during the PM elevated episodes. The SOC sources, regional biomass burning, and biogenic sources illustrated a regional long-range transport with similar locations found during the elevated pollution episodes compared to the normal situations.

Assessment of fire emission inventories for simulating particulate matter in Upper Southeast Asia using WRF-CMAQ

Biomass burning is a major source of particulate matter pollution in Upper Southeast Asia (U-SEA), and accurate fire emissions estimates are of great importance in predicting air quality. In this work, we assess two global fire emission inventories, the Fire INventory from NCAR (FINN) and the Global Fire Assimilation System (GFAS), using the WRF-CMAQ modeling system to simulate aerosol in U-SEA. Simulated particulate matter is compared to observational data from satellites and ground measurements. This study investigates a biomass burning episode that occurred in March 2012. Both emission inventories show similar temporal and spatial variations in PM; however, the quantities of total PM10 emissions differ greatly between FINN and GFAS, with an approximate fivefold ratio of FINN/GFAS. In the source region, simulated PM10 using FINN (PM10FINN) is greatly overestimated, whereas simulated PM10 using GFAS (PM10GFAS) is considerably underestimated. The monthly PM10 averages of PM10FINN, PM10GFAS and observed PM10 in Northern Thailand are 312, 79 and 143 μg/m3, respectively. This study found considerable uncertainties in both FINN and GFAS emission inventories in U-SEA, and these uncertainties were analyzed in this paper. Fire emission estimations require improved specific calculation parameters (i.e. burned area and fuel loading factor for FINN, conversion factor to estimate dry matter burned from fire radiative power for GFAS, emission factors and plume height) to accurately estimate biomass burning emissions for the U-SEA region.

World air particulate matter: sources, distribution and health effects

Particulate matter (PM) is both a major driver of climate change and a source of toxicity for health. In the upper atmosphere, particulate matter modifies the earth radiation budget, cloud formation and acts as a reaction center for air pollutants. In the lower atmosphere, particulate matter changes atmospheric visibility and alters biogeochemical cycles and meteorology. Most critical effects are observed in ambient air, where particulate matter degrades human health. Here we review the sources, spatial and temporal variability, and toxicity of PM10, the particulate matter having particle sizes 10 micrometers or less in diameter, in world regions. For that we analyzed information from the world wide web and databases from government organizations after the year 2000. Findings show that PM10 is a major risk in both developed and developing countries. This risk is more severe in Asian countries compared to Europe and USA, where decreasing trends are recorded during the last two decades. Meteorological factors modify particulate matter variations at local and regional levels. PM2.5/PM10 ratio provides information of particulate matter sources under different environment conditions. Crustal matter, road traffic and combustion of fuels are major sources of particulate matter pollution. Health studies indicate that long-term exposure to particulate matter has multiple health effects in people from all age groups. Identification of possible sources and their control with regular epidemiological monitoring could decrease the impact of particulate matter pollution.

Application of meteorology-based methods to determine local and external contributions to particulate matter pollution: A case study in Venice (Italy)

The air quality is influenced by the potential effects of meteorology at meso- and synoptic scales. While local weather and mixing layer dynamics mainly drive the dispersion of sources at small scales, long-range transports affect the movements of air masses over regional, transboundary and even continental scales. Long-range transport may advect polluted air masses from hot-spots by increasing the levels of pollution at nearby or remote locations or may further raise air pollution levels where external air masses originate from other hot-spots. Therefore, the knowledge of ground-wind circulation and potential long-range transports is fundamental not only to evaluate how local or external sources may affect the air quality at a receptor site but also to quantify it. This review is focussed on establishing the relationships among PM2.5 sources, meteorological condition and air mass origin in the Po Valley, which is one of the most polluted areas in Europe. We have chosen the results from a recent study carried out in Venice (Eastern Po Valley) and have analysed them using different statistical approaches to understand the influence of external and local contribution of PM2.5 sources. External contributions were evaluated by applying Trajectory Statistical Methods (TSMs) based on back-trajectory analysis including (i) back-trajectories cluster analysis, (ii) potential source contribution function (PSCF) and (iii) concentration weighted trajectory (CWT). Furthermore, the relationships between the source contributions and ground-wind circulation patterns were investigated by using (iv) cluster analysis on wind data and (v) conditional probability function (CPF). Finally, local source contribution have been estimated by applying the Lenschow' approach.In summary, the integrated approach of different techniques has successfully identified both local and external sources of particulate matter pollution in a European hot-spot affected by the worst air quality.

Quantifying Space Heating Stove Emissions Related to Different Use Patterns in Mongolia

A major source of particulate matter pollution in Mongolia’s capital, Ulaanbaatar, is emissions from traditional coal-burning space-heating stoves. Significant investment has been made to replace traditional highly polluting heating stoves with improved low-emission high-efficiency stoves. Performance testing that has been undertaken to support the selection of replacement stoves is typically based on manufacturers’ recommended operating procedures, which may not be representative of the operating procedures used in homes. The objective of this research is to evaluate factors that influence stove emissions under typical field operating conditions. A highly-instrumented stove testing facility was constructed to allow for rapid and precise adjustment of factors influencing stove performance. Tests were performed using one of the improved stove models currently available in Ulaanbaatar. Complete burn cycles were conducted with coal from the Ulaanbaatar region using various startup parameters, refueling conditions, and fuel characteristics. Measurements were collected simultaneously from undiluted chimney gas, diluted chimney gas, and plume gas drawn from a dilution tunnel above the chimney. Ignition events lead to increased PM emissions with more than 98% of PM mass emitted during the startup and refueling process. However, emissions during refueling are of particular interest, both because refueling is common and because refueling associated emissions appear to be very high. CO emissions are distributed more evenly over the burn cycle, peaking during ignition and late in the burn cycle. We anticipate these results being useful, in combination with behavioral surveys, for quantifying public health outcomes related to the distribution of improved stoves and to identify opportunities for improving and sustaining performance of the new stoves.

Sources of particulate matter pollution in a small New Zealand city

The sources of PM10 in the Tahunanui airshed of Nelson, New Zealand were investigated using positive matrix factorization (PMF) on elemental data obtained from filters collected from September 2008-September 2009. Also, the source(s) of peak, non-winter PM10 concentrations that exceeded the National Environmental Standard for PM10 were investigated using PM10 and meteorological data from 2007–2012 and the PMF results. Seven PM10 sources were identified: biomass burning, motor vehicles, secondary sulfate, marine aerosol, crustal matter, protective coating activities and fertilizer. Overall, biomass burning was the dominant source contributor (35% of PM10). Analyses of PM10 concentration dependences on meteorological variables showed that peak, non-winter PM10 concentrations that occurred under moderate-to-high wind speeds from the southwest were the result of vehicular movements on unsealed roads in an industrial area. From this information, it is possible for Nelson City Council, who manages air quality at Tahunanui, to formulate mitigation strategies to reduce the impact of biomass burning and industrial vehicles on local air quality.

Assessing atmospheric particulate matter distribution based on Saturation Isothermal Remanent Magnetization of herbaceous and tree leaves in a tropical urban environment

Particulate matter (PM) emissions, and the associated human health risks, are likely to continue increasing in urban environments of developing countries like Abidjan (Ivory Cost). This study evaluated the potential of leaves of several herbaceous and tree species as bioindicators of urban particulate matter pollution, and its variation over different land use classes, in a tropical area. Four species well distributed (presence frequencies >90%) over all land use classes, easy to harvest and whose leaves are wide enough to be easily scanned were selected, i.e.: Amaranthus spinosus (Amaranthaceae), Eleusine indica (Poaceae), Panicum maximum (Poaceae) and Ficus benjamina (Moraceae). Leaf sampling of these species was carried out at 3 distances from the road and at 3 height levels. Traffic density was also noted and finally biomagnetic parameters of these leaves were determined. Results showed that Saturation Isothermal Remanent Magnetization (SIRM) of leaves was at least 4 times higher (27.5×10−6A) in the vicinity of main roads and industrial areas than in parks and residential areas. The main potential sources of PM pollution were motor vehicles and industries. The slightly hairy leaves of the herbaceous plant A. spinosus and the waxy leaves of the tree F. benjamina showed the highest SIRM (25×10−6A). Leaf SIRM increased with distance to road (R2>0.40) and declined with sampling height (R2=0.17). The distance between 0 and 5m from the road seemed to be the most vulnerable in terms of PM pollution. This study has showed that leaf SIRM of herbaceous and tree species can be used to assess PM exposure in tropical urban environments.

Short-term associations between sources of particulate matter pollution and emergency hospital admissions in Hong Kong

Background: Despite decades of evidence linking PM pollution with adverse health effects, significant knowledge gap remains regarding the health impacts of PM sources. Like size and chemical compos...

Carbonaceous aerosols in a wood burning community in rural New Zealand

Particulate matter (PM) in Masterton, New Zealand, a rural community with a population of 20 000, was studied during the winter to gain an understanding of the carbonaceous species and their influence on PM concentrations. The average PM10 concentration during the winter was 21.0±14.8 µg m–3, of which 64% was PM2.5. PM2.5 concentrations were found to be the main cause of elevated PM10 concentrations, and were responsible for exceedances of the New Zealand National Environmental Standard for PM10. Carbonaceous species were 47 and 77% of the total PM10 and PM2.5, respectively. Organic carbon (OC) concentrations were always higher than elemental carbon (EC) concentrations. OC and EC concentrations showed excellent correlation with PM2.5 concentrations, suggesting that combustion processes were the dominant source of PM. Stable isotope analysis yielded δ13C values ranging from –24.9 to –27.6‰, which is indicative of wood combustion. Analysis of particle–phase PAHs by thermal desorption GC/MS yielded an average total PAH concentration of 38.9±25.9ng m–3, accounting for 0.3% of the PM2.5. Analysis of all of the results revealed that residential wood burning for domestic heating was the main source of PM pollution in Masterton. The results of this study suggest that a PM10 standard alone, particularly in areas where wood combustion is common, may not be providing an adequate level of protection for the exposed population.

Derivation of motor vehicle tailpipe particle emission factors suitable for modelling urban fleet emissions and air quality assessments

Urban motor vehicle fleets are a major source of particulate matter pollution, especially of ultrafine particles (diameters < 0.1 microm), and exposure to particulate matter has known serious health effects. A considerable body of literature is available on vehicle particle emission factors derived using a wide range of different measurement methods for different particle sizes, conducted in different parts of the world. Therefore, the choice as to which are the most suitable particle emission factors to use in transport modelling and health impact assessments presented as a very difficult task. The aim of this study was to derive a comprehensive set of tailpipe particle emission factors for different vehicle and road type combinations, covering the full size range of particles emitted, which are suitable for modelling urban fleet emissions. A large body of data available in the international literature on particle emission factors for motor vehicles derived from measurement studies was compiled and subjected to advanced statistical analysis, to determine the most suitable emission factors to use in modelling urban fleet emissions. This analysis resulted in the development of five statistical models which explained 86%, 93%, 87%, 65% and 47% of the variation in published emission factors for particle number, particle volume, PM(1), PM(2.5) and PM(10), respectively. A sixth model for total particle mass was proposed but no significant explanatory variables were identified in the analysis. From the outputs of these statistical models, the most suitable particle emission factors were selected. This selection was based on examination of the statistical robustness of the statistical model outputs, including consideration of conservative average particle emission factors with the lowest standard errors, narrowest 95% confidence intervals and largest sample sizes and the explanatory model variables, which were vehicle type (all particle metrics), instrumentation (particle number and PM(2.5)), road type (PM(10)) and size range measured and speed limit on the road (particle volume). A multiplicity of factors need to be considered in determining emission factors that are suitable for modelling motor vehicle emissions, and this study derived a set of average emission factors suitable for quantifying motor vehicle tailpipe particle emissions in developed countries. The comprehensive set of tailpipe particle emission factors presented in this study for different vehicle and road type combinations enable the full size range of particles generated by fleets to be quantified, including ultrafine particles (measured in terms of particle number). These emission factors have particular application for regions which may have a lack of funding to undertake measurements, or insufficient measurement data upon which to derive emission factors for their region. In urban areas motor vehicles continue to be a major source of particulate matter pollution and of ultrafine particles. It is critical that in order to manage this major pollution source methods are available to quantify the full size range of particles emitted for transport modelling and health impact assessments.