A consistent method for modelling PM10 and PM2.5 concentrations across the United Kingdom in 2004 for air quality assessment

A modelling method has been developed to map PM₁₀ and PM_2.5 concentrations across the UK at background and roadside locations. Separate models have been calibrated using gravimetric measurements and Tapered Element Oscillating Microbalance instruments (TEOM) using source apportionments appropriate to the size fractions and sampling methods. Maps have been prepared for a base year of 2004 and predictions have been calculated for 2010 and 2020 on the basis of current policies. Comparisons of the modelling results with air quality regulations suggest that exceedences of the EU Daughter Directive stage 1 24-h limit value for PM₁₀ at the roadside in 2004 will be largely eliminated by 2020. The concentration cap of 25 μg m⁻³ for PM_2.5 proposed within the CAFÉ Directive is expected to be met at all locations. Projections for 2010 and 2020 suggest that the proposed exposure reduction (ER) target is likely to be considerably more stringent and require additional measures beyond current policies. Thus the model results suggest that the balance between the stringency of the concentration cap and the ER target in the proposed directive is appropriate. Measures to achieve greater reductions should therefore have the maximum public health benefit and air quality policy is not driven by the need to reduce concentrations at isolated ‘hotspots’.     

Statistical characterization of atmospheric PM10 and PM2.5 concentrations at a non-impacted suburban site of Istanbul, Turkey

Inhalable particulate matter (PM10) has been monitored at several stations by Istanbul Municipality. On the other hand, information about fine fraction aerosols (PM2.5) in Istanbul atmosphere was not reported. In this study, 86 daily aerosol samples were collected between July 2002 and July 2003. The PM10 annual arithmetic mean value of 47.1 microg m(-3), was lower than the Turkish air quality standard of 60 microg m(-3). On the other hand, this value was found higher than the annual European Union air quality PM(10) standard of 40 microg m(-3). Furthermore, the annual mean concentration of PM2.5 20.8 microg m(-3) was found higher than The United States EPA standard of 15 microg m(-3). The statistics and relationships of fine, coarse, and inhalable particles were studied. Cyclic behavior of the monthly average concentrations of PM10 and PM2.5 data were investigated. Several frequency distribution functions were used to fit the measured data. According to Chi-squared and Kolmogorov-Smirnov tests, the frequency distributions of PM2.5 and PM10 data were found to fit Log-logistic functions.     

Factors affecting the concentrations of PM10 in central Taiwan

In this study, the synoptic weather types that have high probability and low probability of producing PM10 episode are referred to as HPE and LPE, respectively. Multiple linear regressions analysis showed that NO2 was the most important contributor (35.61%) to the concentrations of PM10 for HPE weather. For LPE weather, the season factor had the greatest contribution (48.11%) to the concentrations of PM10. Using the correlation coefficients between the concentrations of PM10 and SO2 or NO2 on HPE and LPE to calculate the increase of PM10 from LPE to HPE, we found that the increase of PM10 owing to the increase of SO2 and NO2 from LPE to HPE was 12.93microg/m3 which was about 51% of the total amount of PM10 increased from LPE to HPE. Results of factor analysis showed that the first component could be attributed to the result of local pollution especially for the weather patterns of types P3 and P6, while the secondary component for the weather patterns of types P1 and P4 can be attributed to the long-range transport of SO2 pollutants from China.     

Rationalisation of the national survey of air pollution monitoring network of the United Kingdom using spatial correlation analysis: A case-study of the Greater London area

Spatial correlation analysis is applied to 24-h concentrations of smoke and sulphur dioxide monitored by the National Survey of Air Pollution in Greater London to demonstrate that day-to-day changes in pollution levels are primarily controlled by regional controls (synoptic weather patterns) rather than local controls (day-to-day changes in emission rates, patterns and characteristics). With such a high degree of correlation existing between temporal variations in 24-h pollution levels at the network stations, rationalisation of the network is suggested. Daily fluctuations of 24-h pollution concentrations at some stations, designated as reference stations, are selected to be representative of the day-to-day variability of pollution at several nearby, now designated as redundant stations. The degree to which pollution levels at redundant stations are deemed represented by reference stations, and this determines the amount of network rationalisation possible, depends upon the level of accuracy required by the user in estimating pollution levels at redundant stations. For a specified accuracy, a greater degree of network rationalisation is possible for monitoring smoke than for sulphur dioxide concentrations. However, in practice, both pollutants are measured at each station using the same equipment so a compromise network rationalisation is needed. Given the recent decision to introduce a rationalised NSAP in urban areas by April 1982, the technique outlined in this paper provides an objective method for achieving such a rationalisation.     

Prediction of PM10 concentrations at urban traffic intersections using semi-empirical box modelling with instantaneous velocity and acceleration

At urban traffic intersections, vehicles frequently stop with idling engines during the red-light period and speed up rapidly during the green-light period. The changes of driving patterns (i.e., idle, acceleration, deceleration and cruising patterns) generally produce uncertain emission. Additionally, the movement of pedestrians and the influence of wind further result in the random dispersion of pollutants. It is, therefore, too complex to simulate the effects of such dynamics on the resulting emission using conventional deterministic causal models.For this reason, a modified semi-empirical box model for predicting the PM₁₀ concentrations on roadsides is proposed in this paper. The model constitutes three parts, i.e., traffic, emission and dispersion components. The traffic component is developed using a generalized force traffic model to obtain the instantaneous velocity and acceleration when vehicles move through intersections. Hence the distribution of vehicle emission in street canyon during the green-light period is calculated. Then the dispersion component is investigated using a semi-empirical box model combining average wind speed, box height and background concentrations. With these considerations, the proposed model is applied and evaluated using measured data at a busy traffic intersection in Mong Kok, Hong Kong. In order to test the performance of the model, two situations, i.e., the data sets within a sunny day and between two sunny days, were selected to examine the model performance. The predicted values are generally well coincident with the observed data during different time slots except several values are overestimated or underestimated. Moreover, two types of vehicles, i.e., buses and petrol cars, are separately taken into account in the study. Buses are verified to contribute most to the emission in street canyons, which may be useful in evaluating the impact of vehicle emissions on the ambient air quality when there is a significant change in a specific vehicular population.     

Patterns and concentrations of PM10 in a mountainous basin region

In this study, continuous data of PM10 (particles with aerodynamic diameter < 10 microns) concentration measurements for a 4-yr period were analyzed. These measurements have been carried out in the Eordea Basin, an industrial area in the northwestern mountainous region of Greece. The annual, monthly, and diurnal patterns are presented and investigated regarding the prevailing meteorological conditions and atmospheric processes that affect the ambient concentrations of PM10. The effect of wind on controlling PM10 concentration is also discussed. Based on the data analysis, an attempt is made to provide useful information about air quality levels, taking into account U.S. Environmental Protection Agency air quality standards.     

Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London

Multilayer perceptron (MLP) neural networks were trained to model hourly NO_x and NO₂ pollutant concentrations in Central London from basic hourly meteorological data. Results have shown that the models perform well when compared to previous attempts to model the same pollutants using regression based models. This work also illustrates that MLP neural networks are capable of resolving complex patterns of source emissions without any explicit external guidance.     

Assessing the representativeness of monitoring data from an urban intersection site in central London,UK

The wind flow field around urban street-building configurations has an important influence on the microscale pollutant dispersion from road traffic, affecting overall dilution and creating localised spatial variations of pollutant concentration. As a result, the “representativeness” of air quality measurements made at different urban monitoring sites can be strongly dependent on the interaction of the local wind flow field with the street-building geometry surrounding the monitor. The present study is an initial attempt to develop a method for appraising the significance of air quality measurements from urban monitoring sites, using a general application computational fluid dynamics (CFD) code to simulate small-scale flow and dispersion patterns around real urban building configurations. The main focus of the work was to evaluate routine CO monitoring data collected by Westminster City Council at an intersection of street canyons at Marylebone Road, Central London. Many monitors in the UK are purposely situated at urban canyon intersections, which are thought to be local “hot spots” of pollutant emissions, however very limited information exists in the literature on the flow and dispersion patterns associated with them. With the use of simple CFD simulations and the analysis of available monitoring data, it was possible to gain insights into the effect of wind direction on the small-scale dispersion patterns at the chosen intersection, and how that can influence the data captured by a monitor. It was found that a change in wind direction could result in an increase or decrease of monitored CO concentration of up to 80%, for a given level of traffic emissions and meteorological conditions. Understanding and de-coupling the local effect of wind direction from monitoring data using the methods presented in this work could prove a useful new tool for urban monitoring data interpretation.     

Measurements of nitrogen dioxide concentrations at rural sites in the United Kingdom using diffusion tubes

Nitrogen dioxide concentrations have been measured at rural sites in the United Kingdom and have revealed a marked spatial variation. The annual mean NO2 concentration varies from approximately 1 microg Nm-3 in Northern Ireland to approximately 7 microg Nm-3 in East Anglia. Though the temporal resolution of the diffusion tube method is limited by exposure periods of 2-4 weeks, it was possible to detect a marked seasonal variation in NO2 concentration at all sites, with higher values in the winter than in the summer. This is in contrast to the small seasonal variation previously observed at sites in London. Sulphur dioxide concentrations were measured daily using a bubbler method and, if expressed in terms of mass of sulphur and nitrogen, the SO2 and NO2 annual mean concentrations were similar. This is in contrast to an S/N ratio of greater than 3 in total UK emissions of SO2 and NOx. It seems likely that this difference is due to a combination of the different spatial distributions and heights of emissions of SO2 and NOx, the influence of local sources of NOx, and the smaller S/N ratio in Continental European emissions.     

The use of receptor modelling and emission inventory data to explain the downward trend in UK PM10 concentrations

Statistically significant downward trends in measured UK annual mean PM₁₀ concentrations have been observed at eight out of the nine urban background monitoring sites between the start of monitoring in 1992 or 1993 and 2000.Site-specific projections of the individual components of measured PM₁₀ concentrations have been derived for the period 1992–2000 at three monitoring sites from receptor modelling results for 1999 monitoring data. Measured annual average PM₁₀ concentrations declined to between 71% and 66% of the 1992 values during this period at the sites studied. The largest contributions to the decline in total PM₁₀ are from secondary particles at London Bloomsbury (40%, 3.4 μg m⁻³, tapered element oscillating microbalance (TEOM)), stationary sources at Belfast Centre (53%, 4.6 μg m⁻³, TEOM) and roadside traffic emissions at Bury Roadside (49%, 5.0 μg m⁻³, TEOM). The good agreement between the projected total PM₁₀ concentrations and measured values for the years 1992–2000 indicate that the combination of the receptor model and the site-specific projections provide a suitably robust method for predicting future PM₁₀ concentrations and the quantification of the impact of possible future policy measures to reduce PM₁₀ concentrations. The good agreement between the projections and measured concentration also provides a useful verification of the trends in emissions inventory estimates for the 1990s.Projections of estimated PM₁₀ concentrations have also been calculated for the London Bloomsbury site for the period from 1970 to 1991. Annual mean concentrations are predicted to have been in the range from 30 to 35 μg m⁻³, TEOM from 1977 to 1991 but much higher at values between 39 and 46 μg m⁻³, TEOM in the early 1970s.     

Measurements and modelling of PM2.5 concentrations near a major road in Kuopio,Finland

A particle measurement campaign was conducted in a suburban environment near a major road in Kuopio, Central Finland from 3 August to 9 September 1999. The mass concentrations of fine particles (PM_2.5) were measured simultaneously at distances of 12, 25, 52 and 87 m from the centre of a major road at a height of 1.8 m, using identical samplers. The concentration measurements were conducted during 16 daytime hours (from 6.00 a.m. to 10.00 p.m.) for 27 days. Traffic flows and relevant meteorological parameters were measured on-site; meteorological measurements from a nearby synoptic weather station were also utilised. We also suggest a preliminary model for predicting the concentrations of PM_2.5 and apply this model in order to analyse the measured data. The regionally and long-range transported contribution was evaluated on the basis of a semi-empirical mathematical model utilising as input values the daily sulphate, nitrate and ammonium measurements at the EMEP stations (Co-operative programme for monitoring and evaluation of the long-range transmission of air pollutants in Europe). The influence of primary vehicular emissions from the nearest roads was evaluated using a roadside emission and dispersion model, CAR-FMI, in combination with a meteorological pre-processing model, MPP-FMI. The contribution of non-exhaust particulate matter emissions (including resuspension of particulate matter from road surfaces) was estimated simply to be directly proportional to the concentrations originating from primary vehicular emissions. Comparison of the predicted results and measurements yields information on the relative importance of various source categories of the measured concentrations of PM_2.5. The regionally and long-range transported contribution, the primary and non-exhaust vehicular emissions, and other sources were estimated to contribute on average 41±6%, 33±6% and 26±7% of the observed PM_2.5 concentrations, respectively. The model presented could also be applied in other European cities for analysing the source contributions to measured fine particulate matter concentrations.     

Atmospheric concentrations of ammonia and ammonium at an agricultural site in the southeast United States

In this study, we present ∼1 yr (October 1998–September 1999) of 12-hour mean ammonia (NH₃), ammonium (NH₄⁺), hydrochloric acid (HCl), chloride (Cl⁻), nitrate (NO₃⁻), nitric acid (HNO₃), nitrous acid (HONO), sulfate (SO₄²⁻), and sulfur dioxide (SO₂) concentrations measured at an agricultural site in North Carolina's Coastal Plain region. Mean gas concentrations were 0.46, 1.21, 0.54, 5.55, and 4.15 μg m⁻³ for HCl, HNO₃, HONO, NH₃, and SO₂, respectively. Mean aerosol concentrations were 1.44, 1.23, 0.08, and 3.37 μg m⁻³ for NH₄⁺, NO₃⁻, Cl⁻, and SO₄²⁻, respectively. Ammonia, NH₄⁺, HNO₃, and SO₄²⁻ exhibit higher concentrations during the summer, while higher SO₂ concentrations occur during winter. A meteorology-based multivariate regression model using temperature, wind speed, and wind direction explains 76% of the variation in 12-hour mean NH₃ concentrations (n=601). Ammonia concentration increases exponentially with temperature, which explains the majority of variation (54%) in 12-hour mean NH₃ concentrations. Dependence of NH₃ concentration on wind direction suggests a local source influence. Ammonia accounts for >70% of NH_x (NH_x=NH₃+NH₄⁺) during all seasons. Ammonium nitrate and sulfate aerosol formation does not appear to be NH₃ limited. Sulfate is primarily associated ammonium sulfate, rather than bisulfate, except during the winter when the ratio of NO₃⁻–NH₄⁺ is ∼0.66. The annual average NO₃⁻–NH₄⁺ ratio is ∼0.25.     

Estimating the contribution of industrial facilities to annual PM10 concentrations at industrially influenced sites

If measures to reduce the industrial discharge of PM10 shall be planned with high accuracy, a first step must be to estimate the contribution of single industrial facilities to the overall PM10 burden as accurately as possible. In northern Duisburg as an example, an area where iron and steel producing industry is concentrated, PM10 was measured at 4 sampling sites very close to an industrial complex of blast furnaces, a sinter plant, oxygen steel works and a coke oven plant for 9 months in 2006. At two sites metals in PM10 were determined. The results, together with analytical data of urban background sites in the region and data of wind direction and wind speed were used for an estimation of the contribution of single plants to the PM10 burden. A careful analysis of the data showed, that the data of PM10, calcium, iron and zinc measured at two sites close to the industrial area and information about the urban background aerosol were sufficient to calculate the PM10 contribution of the main single plants. The data could be compared with those of modelling.     

PM2.5–10, PM2.5 and associated water-soluble inorganic species at a coastal urban site in the metropolitan region of Rio de Janeiro

The concentrations of PM_2.5−10, PM_2.5 and associated water-soluble inorganic species (WSIS) were determined in a coastal site of the metropolitan region of Rio de Janeiro, Southeastern Brazil, from October 1998 to September 1999 (n=50). Samples were dissolved in water and analyzed for major inorganic ions. The mean (± standard deviation; median) concentrations of PM_2.5−10 and PM_2.5 were, respectively, 26 (± 16; 21) μg m⁻³ and 17 (± 13; 14) μg m⁻³. Their mean concentrations were 1.7–1.8 times higher in dry season (May–October) than in rainy season (November–April). The WSIS comprised, respectively, 34% and 28% of the PM_2.5−10 and PM_2.5 masses. Chloride, Na⁺ and Mg²⁺ were the predominant ions in PM_2.5−10, indicating a significant influence of sea-salt aerosols. In PM_2.5, SO₄²⁻ (∼97% nss-SO₄²⁻) and NH₄⁺ were the most abundant ions and their equivalent concentration ratio (SO₄²⁻/NH₄⁺ ∼1.0) suggests that they were present as (NH₄)₂SO₄ particles. The mean concentration of (NH₄)₂SO₄ was 3.4 μg m⁻³. The mean equivalent PM_2.5 NO₃⁻ concentration was eight times smaller than those of SO₄²⁻ and NH₄⁺. The PM_2.5 NO₃⁻ concentration in dry season was three times higher than in rainy season, probably due to reaction of NaCl (sea salt) with HNO₃ as a result of higher levels of NO_y during the dry season and/or reduced volatilization of NH₄NO₃ due to lower wintertime temperature. Chloride depletion was observed in both size ranges, although more pronouncely in PM_2.5.     

Contribution of railway traffic to local PM10 concentrations in Switzerland

Field measurement campaigns of PM10 and its elemental composition (daily sampling on filters) covering different seasons were performed at two sites near the busiest railway station of Switzerland in Zurich (at a distance of 10 m from the tracks) and at a site near a very busy railway line with more than 700 trains per day. At this latter site parallel samples were taken at 10, 36 and 120 m distances from the tracks with the aim to study the distance dependence of the railway induced PM10 concentrations.To distinguish the relatively small railway emissions from the regional background (typically 20–25 μg m⁻³), simultaneous samples were also taken at an urban background site in Zurich. The differences in PM10 and elemental concentrations between the railway exposed sites and the background site were allocated to the railway contribution.Small, however, measurable PM10 concentration differences were found at all sites. The elemental composition of these differences revealed iron as the only quantitatively important constituent. As a long-term average it amounted to approximately 1 μg m⁻³ Fe at a distance of 10 m from the tracks at all three sites. Assuming that iron was at least partly oxidised (e.g. in the form of Fe₂O₃) the contribution can amount up to 1.5 μg m⁻³. Emissions of copper, manganese and chromium from trains were also clearly identified. However, compared to iron these, elements were emitted in very low quantities.No significant contribution from rock material (calcium, aluminium, magnesium, sodium) was observed as might have been expected from erosion, abrasion and resuspension from the gravel below the tracks. Particle emissions from diesel exhaust were not considered as trains in Switzerland are operated nearly exclusively by electric locomotives.The railway, induced contribution to ambient PM10 decreased rapidly with increasing distance from the tracks. At a distance of 120 m this contribution dropped to only 25% of the contribution observed at 10 m distance.     

Spatial and seasonal variation of PM10 mass concentrations in Taiwan

Hourly data of PM10 concentration collected from an air quality-monitoring network has been analyzed over Taiwan from 1994 to 1999. Fourteen sites from 72 monitoring stations were selected to evaluate the spatial and seasonal variations in the regions of north, southwest, south, east and National Park. The selected monitoring sites are located in a suburban environment, except Nantz and Linyuan that are located in industrial areas. Moreover, Yangming and Hengchuen are located in National Park. Spatial and seasonal variations of PM10 concentrations are rather large over Taiwan. Annual average in south is approximately six times higher than in National parks. In northern sites, the highest concentration occurs in March–May, which is attributed to the occurrence of dust storms in arid regions of central Asia and the transport of dust by northeasterly monsoon. A marked seasonal variation of PM10 concentrations can be observed both in southwestern and southern regions. The pattern is characterized by high concentrations in winter and low in summer. Appearance of the highest monthly PM10 concentration in winter of south may be in part due to the lowest number of monthly precipitation days and low temperature, both of which occurred in winter. The frequency of PM10 daily mean concentration for exceeded 150 μg m⁻³ is 15% during winter in south, which reflects the serious pollution problem there. Monitoring sites in National Park are representatives of remote environments, but the PM10 concentrations are still affected by the dust storms and human activities.     

Analysis of PM10, PM2.5, and PM2 5-10 concentrations in Santiago, Chile, from 1989 to 2001

Daily particle samples were collected in Santiago, Chile, at four urban locations from January 1, 1989, through December 31, 2001. Both fine PM with da < 2.5 microm (PM2.5) and coarse PM with 2.5 < da < 10 microm (PM2.5-10) were collected using dichotomous samplers. The inhalable particle fraction, PM10, was determined as the sum of fine and coarse concentrations. Wind speed, temperature and relative humidity (RH) were also measured continuously. Average concentrations of PM2.5 for the 1989-2001 period ranged from 38.5 microg/m3 to 53 microg/m3. For PM2.5-10 levels ranged from 35.8-48.2 microg/m3 and for PM10 results were 74.4-101.2 microg/m3 across the four sites. Both annual and daily PM2.5 and PM10 concentration levels exceeded the U.S. National Ambient Air Quality Standards and the European Union concentration limits. Mean PM2.5 levels during the cold season (April through September) were more than twice as high as those observed in the warm season (October through March); whereas coarse particle levels were similar in both seasons. PM concentration trends were investigated using regression models, controlling for site, weekday, month, wind speed, temperature, and RH. Results showed that PM2.5 concentrations decreased substantially, 52% over the 12-year period (1989-2000), whereas PM2.5-10 concentrations increased by approximately 50% in the first 5 years and then decreased by a similar percentage over the following 7 years. These decreases were evident even after controlling for significant climatic effects. These results suggest that the pollution reduction programs developed and implemented by the Comisión Nacional del Medio Ambiente (CONAMA) have been effective in reducing particle levels in the Santiago Metropolitan region. However, particle levels remain high and it is thus imperative that efforts to improve air quality continue.     

Particulate matter in California: part 1--Intercomparison of several PM2.5, PM10-2.5, and PM10 monitoring networks

It will be many years before the recently deployed network of fine particulate matter with an aerodynamic diameter less than 2.5 microm (PM2.5) Federal Reference Method (FRM) samplers produces information on nonattainment areas, trends, and source impacts. However, data on PM2.5 and its major constituents have been routinely collected in California for the past 20 years. The California Air Resources Board operated as many as 20 dichotomous (dichot) samplers for PM2.5 and coarse PM (PM10-2.5). The California Acid Deposition Monitoring Program (CADMP) collected 12-h-average PM2.5 and PM10 from 1988 to 1995 at ten urban and rural sites and 24-h-average PM2.5 at five urban sites since 1995. Beginning in 1994, the Children's Health Study collected 2-week averages of PM2.5 in 12 communities in southern California using the Two-Week Sampler (TWS). Comparisons of collocated samples establish relationships between the dichot, CADMP, and TWS samplers and the 82-site network of PM2.5 FRM samplers deployed since 1999 in California. PM mass data from the different monitoring programs have modest to high correlation to FRM mass data, fairly small systematic biases and negative proportional biases ranging from 7 to 22%. If the biases are taken into account, all of the programs should be considered comparable with the FRM program. Thus, historical data can be used to develop long-term PM trends in California.     

Source apportionment of PM2.5 and PM10 aerosols in Brisbane (Australia) by receptor modelling

Aerosol samples for PM_2.5 and PM₁₀ (particulate matter with aerodynamic diameters less than 2.5 and 10 μm, respectively) were collected from 1993 to 1995 at five sites in Brisbane, a subtropical coastal city in Australia. This paper investigates the contributions of emission sources to PM_2.5 and PM₁₀ aerosol mass in Brisbane. Source apportionment results derived from the chemical mass balance (CMB), target transformation factor analysis (TTFA) and multiple linear regression (MLR) methods agree well with each other. The contributions from emission sources exhibit large variations in particle size with temporal and spatial differences. On average, the major contributors of PM₁₀ aerosol mass in Brisbane include: soil/road side dusts (25% by mass), motor vehicle exhausts (13%, not including the secondary products), sea salt (12%), Ca-rich and Ti-rich compounds (11%, from cement works and mineral processing industries), biomass burning (7%), and elemental carbon and secondary products contribute to around 15% of the aerosol mass on average. The major sources of PM_2.5 aerosols at the Griffith University (GU) site (a suburban site surrounded by forest area) are: elemental carbon (24% by mass), secondary organics (21%), biomass burning (15%) and secondary sulphate (14%). Most of the secondary products are related to motor vehicle exhausts, so, although motor vehicle exhausts contribute directly to only 6% of the PM_2.5 aerosol mass, their total contribution (including their secondary products) could be substantial. This pattern of source contribution is similar to the results for Rozelle (Sydney) among the major Australian studies, and is less in contributions from industrial and motor vehicular exhausts than the other cities. An attempt was made to estimate the contribution of rural dust and road side dust. The results show that road side dusts could contribute more than half of the crustal matter. More than 80% of the contribution of vehicle exhausts arises from diesel-fuelled trucks/buses. Biomass burning, large contributions of crustal matter, and/or local contributing sources under calm weather conditions, are often the cause of the high PM₁₀ episodes at the GU site in Brisbane.     

Estimating the emission source reduction of PM10 in central Taiwan

Three theoretical parent frequency distributions; lognormal, Weibull and gamma were used to fit the complete set of PM10 data in central Taiwan. The gamma distribution is the best one to represent the performance of high PM10 concentrations. However, the parent distribution sometimes diverges in predicting the high PM10 concentrations. Therefore, two predicting methods, Method I: two-parameter exponential distribution and Method II: asymptotic distribution of extreme value, were used to fit the high PM10 concentration distributions more correctly. The results fitted by the two-parameter exponential distribution are better matched with the actual high PM10 data than that by the parent distributions. Both of the predicting methods can successfully predict the return period and exceedances over a critical concentration in the future year. Moreover, the estimated emission source reductions of PM10 required to meet the air quality standard by Method I and Method II are very close. The estimated emission source reductions of PM10 range from 34% to 48% in central Taiwan.