Variance In PM2 Research Articles

Estimation of historical daily PM2.5 concentrations for three Chinese megacities: Insight into the socioeconomic factors affecting PM2.5

Studying historical changes in the 2.5 μm particulate matter concentration (PM2.5) can clarify the relationship between air pollution and socioeconomic development. Daily PM2.5 levels and meteorological data (2012–2022) for three large cities (Beijing, Shanghai, and Xi'an) at different development stages in different regions of China were used to construct a random forest (RF) model for estimating historical PM2.5 data for the period 1973–2011, a time period in which few measurements were made. The eigenvalue for visibility was the largest in the RF model; visibility explained 0.76–0.87 of the variance in PM2.5 for the three cities. The daily estimated PM2.5 was validated, with an R2 of 0.654–0.780 and average absolute error of 11.52–31.73 μg m−3 in the model. PM2.5 concentrations predicted by the RF model for 2004–2011 were highly correlated with gravimetric measurements (R = 0.585, p < 0.01). We extensively validated the results of RF using manual weighing PM2.5 data, online monitoring concentration of PM10, and aerosol optical depth (AOD), demonstrating the accuracy of the model. Over the study period, the PM2.5 level first increased and then decreased in the three cities; however, the year at which the trend changed differed. We further explored the effects of urbanization and economic growth on PM2.5 levels by investigating the correlations between socioeconomic indicators and PM2.5. The magnitude of the permanent population of Beijing and gross regional production growth in Shanghai were both significantly positively correlated with the PM2.5 level. Increasing the size of urban green areas can reduce PM2.5; this effect was strongest for the southern city of Shanghai, may due to their different climates and green tree species. Energy consumption and emissions from primary industries were strongly positively correlated with the urban PM2.5 level. An in-depth understanding of the factors affecting PM2.5 concentrations could help policymakers improve air quality management strategies, especially for densely populated megacities.

High-resolution patterns and inequalities in ambient fine particle mass (PM2.5) and black carbon (BC) in the Greater Accra Metropolis, Ghana

Growing cities in sub-Saharan Africa (SSA) experience high levels of ambient air pollution. However, sparse long-term city-wide air pollution exposure data limits policy mitigation efforts and assessment of the health and climate effects. In the first study of its kind in West Africa, we developed high resolution spatiotemporal land use regression (LUR) models to map fine particulate matter (PM2.5) and black carbon (BC) concentrations in the Greater Accra Metropolitan Area (GAMA), one of the fastest sprawling metropolises in SSA. We conducted a one-year measurement campaign covering 146 sites and combined these data with geospatial and meteorological predictors to develop separate Harmattan and non-Harmattan season PM2.5 and BC models at 100 m resolution. The final models were selected with a forward stepwise procedure and performance was evaluated with 10-fold cross-validation. Model predictions were overlayed with the most recent census data to estimate the population distribution of exposure and socioeconomic inequalities in exposure at the census enumeration area level. The fixed effects components of the models explained 48–69 % and 63–71 % of the variance in PM2.5 and BC concentrations, respectively. Spatial variables related to road traffic and vegetation explained the most variability in the non-Harmattan models, while temporal variables were dominant in the Harmattan models. The entire GAMA population is exposed to PM2.5 levels above the World Health Organization guideline, including even the Interim Target 3 (15 μg/m3), with the highest exposures in poorer neighborhoods. The models can be used to support air pollution mitigation policies, health, and climate impact assessments. The measurement and modelling approach used in this study can be adapted to other African cities to bridge the air pollution data gap in the region.

Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation.

Machine learning models can emulate chemical transport models, reducing computational costs and enabling more experimentation. We developed emulators to predict annual−mean fine particulate matter (PM2.5) and ozone (O3) concentrations and their associated chronic health impacts from changes in five major emission sectors (residential, industrial, land transport, agriculture, and power generation) in China. The emulators predicted 99.9% of the variance in PM2.5 and O3 concentrations. We used these emulators to estimate how emission reductions can attain air quality targets. In 2015, we estimate that PM2.5 exposure was 47.4 μg m−3 and O3 exposure was 43.8 ppb, associated with 2,189,700 (95% uncertainty interval, 95UI: 1,948,000–2,427,300) premature deaths per year, primarily from PM2.5 exposure (98%). PM2.5 exposure and the associated disease burden were most sensitive to industry and residential emissions. We explore the sensitivity of exposure and health to different combinations of emission reductions. The National Air Quality Target (35 μg m−3) for PM2.5 concentrations can be attained nationally with emission reductions of 72% in industrial, 57% in residential, 36% in land transport, 35% in agricultural, and 33% in power generation emissions. We show that complete removal of emissions from these five sectors does not enable the attainment of the WHO Annual Guideline (5 μg m−3) due to remaining air pollution from other sources. Our work provides the first assessment of how air pollution exposure and disease burden in China varies as emissions change across these five sectors and highlights the value of emulators in air quality research.

Assessment of Fine Particulate Matter for Port City of Eastern Peninsular India Using Gradient Boosting Machine Learning Model

An assessment and prediction of PM2.5 for a port city of eastern peninsular India is presented. Fifteen machine learning (ML) regression models were trained, tested and implemented to predict the PM2.5 concentration. The predicting ability of regression models was validated using air pollutants and meteorological parameters as input variables collected from sites located at Visakhapatnam, a port city on the eastern side of peninsular India, for the assessment period 2018–2019. Highly correlated air pollutants and meteorological parameters with PM2.5 concentration were evaluated and presented during the period under study. It was found that the CatBoost regression model outperformed all other employed regression models in predicting PM2.5 concentration with an R2 score (coefficient of determination) of 0.81, median absolute error (MedAE) of 6.95 µg/m3, mean absolute percentage error (MAPE) of 0.29, root mean square error (RMSE) of 11.42 µg/m3 and mean absolute error (MAE) of 9.07 µg/m3. High PM2.5 concentration prediction results in contrast to Indian standards were also presented. In depth seasonal assessments of PM2.5 concentration were presented, to show variance in PM2.5 concentration during dominant seasons.

Spatio-temporal variability and persistence of PM2.5 concentrations in China using trend analysis methods and Hurst exponent

Studying the persistence and spatial-temporal trends of air pollution is beneficial for determining the pollutant risk area and enables the development of associated prediction tools and models. Relying on the PM2.5 concentrations data retrieved via remote sensing from 2000 to 2018, the spatial and temporal pattern, variation tends, and persistence is determined through the Theil-Sen median trend analysis, Mann-Kendall, and Hurst exponent. We combine the Theil-Sen Median + Mann-Kendall and Hurst to quantitatively and qualitatively predict the future trends of China's PM2.5 concentrations as a new perspective. Results reveal that PM2.5 concentrations increased at first and then decreased significantly, with 2009–2011 as the turning point for PM2.5 pollution changes, particularly in Central China and the Southeast Coastal Area. The area where PM2.5 concentrations were below 10 μg/m3 account for 29.75% of China's total territory, reaching the annual average criterion value determined by the World Health Organization. The areas presenting a continuous increase (15.69%) and decline (17.46%) of PM2.5 concentrations were almost equal. As a result, the constant monitoring of the variance in PM2.5 concentrations in the sustainably increased and underdetermined regions, such as Tibet and Northeast China, is needed. This study used simulated PM2.5 concentrations data as a valuable complement to China's ground monitoring stations, thus compensating for a shortage of long-term series data. Grid data analysis can more finely show the interior disputes in PM2.5 concentrations. The algorithm codes can be freely downloaded and become a helpful tool for analyzing the spatio-temporal variation characteristics of primary air pollutants.

A survey of PM2.5 preventive behavioral intention and related factors among community elderly in Northern Taiwan.

Population aging and air pollution are global concerns. The purpose of this study is to explore the relationship among particulate matter with a diameter of 2.5 μm or less (PM2.5) knowledge, PM2.5 preventive attitude, and PM2.5 preventive behavioral intention in the elderly.A cross-sectional survey design was applied in this study, including usage intention and snowball sampling. A total of 617 elderly people aged over 65 participated, and the collected data were quantitatively analyzed.The results showed that the mean score of PM2.5 knowledge of the elderly was 10.34 (79.53%) with the standard deviation (SD) of 3.42, the mean score of PM2.5 preventive attitude was 4.58 (91.60%) with SD of 0.49, and the mean score of PM2.5 preventive behavioral intention was 4.72 (94.40%) with SD of 0.40. Elderly people's characteristics regarding PM2.5 knowledge and PM2.5 preventive attitude explained 22% (adjusted R2 = 0.22, F = 30.44, P < .001) of the variance in PM2.5 preventive behavioral intention.It is concluded that we found no difference in PM2.5 knowledge, PM2.5 preventive attitude, or PM2.5 preventive behavioral intention among the elderly with or without chronic diseases. In our opinion, health education regarding the threat of PM2.5 to the health of the elderly should be strengthened, to enhance their knowledge, preventive attitude, and preventive behavioral intention of PM2.5.

Statistical Emulation of Winter Ambient Fine Particulate Matter Concentrations From Emission Changes in China.

Air pollution exposure remains a leading public health problem in China. The use of chemical transport models to quantify the impacts of various emission changes on air quality is limited by their large computational demands. Machine learning models can emulate chemical transport models to provide computationally efficient predictions of outputs based on statistical associations with inputs. We developed novel emulators relating emission changes in five key anthropogenic sectors (residential, industry, land transport, agriculture, and power generation) to winter ambient fine particulate matter (PM2.5) concentrations across China. The emulators were optimized based on Gaussian process regressors with Matern kernels. The emulators predicted 99.9% of the variance in PM2.5 concentrations for a given input configuration of emission changes. PM2.5 concentrations are primarily sensitive to residential (51%–94% of first‐order sensitivity index), industrial (7%–31%), and agricultural emissions (0%–24%). Sensitivities of PM2.5 concentrations to land transport and power generation emissions are all under 5%, except in South West China where land transport emissions contributed 13%. The largest reduction in winter PM2.5 exposure for changes in the five emission sectors is by 68%–81%, down to 15.3–25.9 μg m−3, remaining above the World Health Organization annual guideline of 10 μg m−3. The greatest reductions in PM2.5 exposure are driven by reducing residential and industrial emissions, emphasizing the importance of emission reductions in these key sectors. We show that the annual National Air Quality Target of 35 μg m−3 is unlikely to be achieved during winter without strong emission reductions from the residential and industrial sectors.

Variability of exposure to PM2.5 related to indoor pollution sources in low socio-economic urban households, Durban, South Africa

TPS 722: Low and middle income countries: indoor air pollutants, Exhibition Hall, Ground floor, August 28, 2019, 3:00 PM - 4:30 PM Abstract Background: The goal of PM2.5 indoor assessment for use in epidemiological studies is to estimate indoor levels over time and space. Because of logistical constraints, very few studies have been conducted to assess the reproducibility of indoor PM2.5 measurements. Objective: To evaluate PM2.5 variability in urban households of mothers participating in the Mother and Child in the Environment birth cohort study in Durban, South Africa. Methods: Indoor PM2.5 samples were collected in warm season and cold season from 30 homes across Durban. Four measurements of PM2.5 were undertaken in each house within a 2 week interval using Airmetrics miniVol samplers. We fitted a mixed effects model to estimate the variation of PM2.5 and to determine the determinants of this variance. Results: PM2.5 levels ranged from 1.5 to 302 µg/m3. Levels were highest (mean (SD) =60.8 (70.8) µg/m3) during the cold season and lowest (mean (SD)=43.5 (45.8) µg/m3) during the warm season. Within-home variation dominated the total variability. The results showed that PM2.5 predictors such as active smoking, environmental tobacco smoke exposure and seasonal indicators were significant determinants of PM2.5 variation (R2=0.71), contributing to 2.5–81.4% of the variability. Conclusions: The results suggest that within home variance in PM2.5 levels influence total variability more than factors affecting PM2.5 levels variance between homes over time.

Investigating the influence of meteorological factors on particulate matters: A case study based on path analysis

The meteorological factors play an important role to influence the concentration of the particulate matters. The path analysis method is employed to investigate the influence of meteorological factors (including atmospheric temperature ( AT), relative humidity ( RH), and wind speed ( WS)) on particulate matters (including PM2.5 and PM10) in Dazhou city. The following results are obtained: (1) The direct path coefficients of AT, RH, and WS to PM2.5 and PM10 are all negative, which means that the concentration of particulate matters would be declined following with the increasing of AT, RH, and WS. (2) The meteorological factors would explain about 17.43 and 16.52% variance of PM2.5 and PM10, respectively. However, 82.57% variance of PM2.5 and 83.48% variance of PM10 would be determined by non-meteorological factors. (3) AT and WS are the most important meteorological factors to modulate the concentration of particulate matters. AT would explain 12.73 and 8.78% variance of PM2.5 and PM10, respectively. WS would explain 6.54 and 8.69% variance of PM2.5 and PM10, respectively. (4) According to the absolute value of the determination coefficients, the main influence on the concentration of PM2.5 is the direct influence by AT and the impact on the concentration of PM10 is by the combined contribution of meteorological factors.

Investigating the influence of urban land use and landscape pattern on PM2.5 spatial variation using mobile monitoring and WUDAPT

Particulate matter that <2.5 µm in aerodynamic diameter (PM2.5) has been recognized as one of the principal pollutants that degrades air quality and increases health burdens. In this study, we employ the MLR and GWR modelling method to obtain estimation models for PM2.5 with a set of land use/landscape metrics as predictor variables. The study focused on investigating the influence of urban land use and landscape pattern on PM2.5 spatial variation, specifically, on identification of influential landscape classes/types that regulate PM2.5 concentration levels. The spatial PM2.5 concentration in the compact urban scenario of Hong Kong was sampled by conducting a series of mobile monitoring campaigns. The Local Climate Zone (LCZ) Scheme and World Urban Database and Portal Tools (WUDAPT) level 0 database were adopted as the basis of the calculation of land use/landscape metrics. These metrics were then adopted as the predictors to explain the spatial variations in PM2.5. 62% of the variance in PM2.5 can be explained by the resultant GWR model using only five land use/landscape classes, and without using any traffic-related variables or data from emission inventory. The findings can inform the urban planning strategies for mitigating air pollution and also indicate the usefulness of LCZ and WUDAPT in estimating the spatial variation of urban air quality.

Insignificant effect of climate change on winter haze pollution in Beijing

Abstract. Several recent studies have suggested that 21st century climate change will significantly worsen the meteorological conditions, leading to very high concentrations of fine particulate matter (PM2.5) in Beijing in winter (Beijing haze). We find that 81 % of the variance in observed monthly PM2.5 during 2010–2017 winters can be explained by a single meteorological mode, the first principal component (PC1) of the 850 hPa meridional wind velocity (V850) and relative humidity (RH). V850 and RH drive stagnation and chemical production of PM2.5, respectively, and thus have a clear causal link to Beijing haze. PC1 explains more of the variance in PM2.5 than either V850 or RH alone. Using additional meteorological variables does not explain more of the variance in PM2.5. Therefore PC1 can serve as a proxy for Beijing haze in the interpretation of long-term climate records and in future climate projections. Previous studies suggested that shrinking Arctic sea ice would worsen winter haze conditions in eastern China, but we show with the PC1 proxy that Beijing haze is correlated with a dipole structure in the Arctic sea ice rather than with the total amount of sea ice. Beijing haze is also correlated with dipole patterns in Pacific sea surface temperatures (SSTs). We find that these dipole patterns of Arctic sea ice and Pacific SSTs shift and change sign on interdecadal scales, so that they cannot be used reliably as future predictors for the haze. Future 21st century trends of the PC1 haze proxy computed from the CMIP5 ensemble of climate models are statistically insignificant. We conclude that climate change is unlikely to significantly offset current efforts to decrease Beijing haze through emission controls.

Fine-Scale Source Apportionment Including Diesel-Related Elemental and Organic Constituents of PM2.5 across Downtown Pittsburgh.

Health effects of fine particulate matter (PM2.5) may vary by composition, and the characterization of constituents may help to identify key PM2.5 sources, such as diesel, distributed across an urban area. The composition of diesel particulate matter (DPM) is complicated, and elemental and organic carbon are often used as surrogates. Examining multiple elemental and organic constituents across urban sites, however, may better capture variation in diesel-related impacts, and help to more clearly separate diesel from other sources. We designed a “super-saturation” monitoring campaign of 36 sites to capture spatial variance in PM2.5 and elemental and organic constituents across the downtown Pittsburgh core (~2.8 km2). Elemental composition was assessed via inductively-coupled plasma mass spectrometry (ICP-MS), organic and elemental carbon via thermal-optical reflectance, and organic compounds via thermal desorption gas-chromatography mass-spectrometry (TD-GCMS). Factor analysis was performed including all constituents—both stratified by, and merged across, seasons. Spatial patterning in the resultant factors was examined using land use regression (LUR) modelling to corroborate factor interpretations. We identified diesel-related factors in both seasons; for winter, we identified a five-factor solution, describing a bus and truck-related factor [black carbon (BC), fluoranthene, nitrogen dioxide (NO2), pyrene, total carbon] and a fuel oil combustion factor (nickel, vanadium). For summer, we identified a nine-factor solution, which included a bus-related factor (benzo[ghi]fluoranthene, chromium, chrysene, fluoranthene, manganese, pyrene, total carbon, total elemental carbon, zinc) and a truck-related factor (benz[a]anthracene, BC, hopanes, NO2, total PAHs, total steranes). Geographic information system (GIS)-based emissions source covariates identified via LUR modelling roughly corroborated factor interpretations.

Analysis of PM2.5 in Córdoba, Argentina under the effects of the El Niño Southern Oscillation

In this work, PM2.5 samples were collected in the winter-spring months of 2014–2016 at an urban site in Córdoba. Córdoba is the second largest city in Argentina and is an important industrial and touristic center. The collected samples were individually analyzed for chemical composition using different techniques. The soluble inorganic ions and carbonaceous particles were determined from bulk aerosol samples for the first time in the city. The mass concentrations of PM2.5, organic carbon, elemental carbon, inorganic ions and metals were determined according to the mass balance. The dominant mass components were organic matter and elemental carbon (54.8%), mineral dust (6.1%), secondary inorganic aerosols (3.0%), and salt (1.2%). A principal component analysis was applied to the samples and resulted in five major factors that explained 79% of the variance in PM2.5. These factors represented combustion, industrial sources, soil dust, secondary inorganic aerosol, and salt, and each explained between 11% and 20% of the variance. A comparison with the results from a previous campaign (2010–2011) revealed appreciable changes in the PM2.5 chemical composition. These changes were attributed to the two extreme meteorological conditions that prevailed in the region. The years 2014–2016 were largely dominated by the warm phase of the El Niño–Southern Oscillation, which leads to humid and cold weather in the Córdoba region, while the samples from 2010 to 2011 were collected during the dry and hot years resulting from the La Niña regime.

Spatiotemporal estimation of historical PM2.5 concentrations using PM10, meteorological variables, and spatial effect

Monitoring of fine particulate matter with diameter <2.5 μm (PM2.5) started from 1999 in the US and even later in many other countries. The lack of historical PM2.5 data limits epidemiological studies of long-term exposure of PM2.5 and health outcomes such as cancer. In this study, we aimed to design a flexible approach to reliably estimate historical PM2.5 concentrations by incorporating spatial effect and the measurements of existing co-pollutants such as particulate matter with diameter <10 μm (PM10) and meteorological variables. Monitoring data of PM10, PM2.5, and meteorological variables covering the entire state of California were obtained from 1999 through 2013. We developed a spatiotemporal model that quantified non-linear associations between PM2.5 concentrations and the following predictor variables: spatiotemporal factors (PM10 and meteorological variables), spatial factors (land-use patterns, traffic, elevation, distance to shorelines, and spatial autocorrelation), and season. Our model accounted for regional-(county) scale spatial autocorrelation, using spatial weight matrix, and local-scale spatiotemporal variability, using local covariates in additive non-linear model. The spatiotemporal model was evaluated, using leaving-one-site-month-out cross validation. Our final daily model had an R2 of 0.81, with PM10, meteorological variables, and spatial autocorrelation, explaining 55%, 10%, and 10% of the variance in PM2.5 concentrations, respectively. The model had a cross-validation R2 of 0.83 for monthly PM2.5 concentrations (N = 8170) and 0.79 for daily PM2.5 concentrations (N = 51,421) with few extreme values in prediction. Further, the incorporation of spatial effects reduced bias in predictions. Our approach achieved a cross validation R2 of 0.61 for the daily model when PM10 was replaced by total suspended particulate. Our model can robustly estimate historical PM2.5 concentrations in California when PM2.5 measurements were not available.

Effects of synoptic weather on ground-level PM2.5 concentrations in the United States

It is known that individual meteorological factors affect the concentrations of fine particulate matter with aerodynamic diameters ≤2.5 μm (PM2.5), yet the specific meteorological effects found in previous studies are largely inconsistent and even conflicting. This study investigates influences of daily and short term changes in synoptic weather on ground-level PM2.5 concentrations in a large geographical area (75 cities across the contiguous United States (U.S.)) by using ten-year (2001–2010) spatial synoptic classification (SSC) data. We find that in the spring, summer, and fall the presence of the tropical weather types (i.e., dry-tropical (DT) and moist-tropical (MT)) is likely to associate with significantly higher levels of PM2.5 as compared to an all-weather-type-day average, and the presence of the polar weather types (i.e., dry-polar (DP) and moist-polar (MP)) is associated with significantly lower PM2.5 concentrations. The short-term (day to day) changes in synoptic weather types in a region are also likely to lead to significant variance in PM2.5 concentrations. For example, the largest increase in PM2.5 concentration occurs with the synoptic weather type changing from DP-to-MT. Conversely, a MT-to-DP weather type change results in the largest decrease in PM2.5 concentrations. Compared to air temperature, the effects of atmospheric moisture on PM2.5 concentration tend to be subtle, demonstrating that in conjunction with moderate temperature, neither the dry nor the moist air (except moist-moderate (MM) in summer) are associated with significantly high or low PM2.5 concentrations. Finally, we find that the effects of the synoptic weather type on PM2.5 concentrations may vary for different seasons and geographical areas. These findings suggest that interactions between atmospheric factors and seasonal and/or geographical factors have considerable impacts on the PM2.5 concentrations, and therefore should be considered in addition to the SSC when conducting environment health assessments.

Assessing exposure to household air pollution: A systematic review and meta-analysis of carbon monoxide as a surrogate measure of particulate matter

Introduction: Particulate matter (PM2.5) and carbon monoxide (CO) are co-emitted from household biomass burning. PM2.5 is most strongly associated with health outcomes but its measurement is costly and logistically difficult. CO is less costly and easier to measure and has thus been widely used as a surrogate of PM2.5 in epidemiologic studies and trials. However, evidence for its validity as a surrogate is limited and conflicting. Methods: We identified 62 peer-reviewed studies with paired PM2.5 and CO exposures or indoor concentrations. We reviewed study characteristics including country, study population, urbanicity, fuel/stove types, other air pollution sources, and PM/CO measurement methods and correlations. We also compiled a dataset of &gt;1600 paired PM2.5 and CO measurements from 18 studies in 8 countries and conducted pooled analyses of the associations between PM2.5 and CO exposures and indoor concentrations. Confounding and effect modification by factors hypothesized to influence PM2.5-CO relationships (i.e., fuel, urbanicity, season, &amp; CO measurement type) were investigated. Results: PM2.5-CO correlations in published studies were lower for personal exposure (range: 0.22-0.71) than for indoor concentrations (range: 0.012-0.95). In pooled analyses, CO explained 29% and 57% of the variance in PM2.5 for personal exposure and indoor concentrations, respectively, after adjusting for covariates and accounting for clustering by study. The CO-PM2.5 relationship varied significantly (p&lt;0.01) by fuel class and setting. Restricting to measurements from heating seasons, rural settings, exclusive biomass users, or electrochemical or diffusion-based CO increased the ability of the fully adjusted model to explain the variation in PM2.5 exposure by 0.5 to 12.7%. Conclusions: Exposure to CO is not a universally valid surrogate for exposure to PM2.5 in studies of household air pollution. This has important implications for exposure assessment in future epidemiologic studies.

An empirical model to estimate daily forest fire smoke exposure over a large geographic area using air quality, meteorological, and remote sensing data

Exposure to forest fire smoke (FFS) is associated with a range of adverse health effects. The British Columbia Asthma Medication Surveillance (BCAMS) product was developed to detect potential impacts from FFS in British Columbia (BC), Canada. However, it has been a challenge to estimate FFS exposure with sufficient spatial coverage for the provincial population. We constructed an empirical model to estimate FFS-related fine particulate matter (PM2.5) for all populated areas of BC using data from the most extreme FFS days in 2003 through 2012. The input data included PM2.5 measurements on the previous day, remotely sensed aerosols, remotely sensed fires, hand-drawn tracings of smoke plumes from satellite images, fire danger ratings, and the atmospheric venting index. The final model explained 71% of the variance in PM2.5 observations. Model performance was tested in days with high, moderate, and low levels of FFS, resulting in correlations from 0.57 to 0.83. We also developed a method to assign the model estimates to geographical local health areas for use in BCAMS. The simplicity of the model allows easy application in time-constrained public health surveillance, and its sufficient spatial coverage suggests utility as an exposure assessment tool for epidemiologic studies on FFS exposure.

Relationships between size segregated mass concentration data and ultrafine particle number concentrations in urban areas

Mass concentration data derived from samples collected with a micro-orifice uniform deposit impactor (MOUDI) in six Australian urban centers during periods of significant particle loading have been used to investigate the relationships between TSP, PM10, PM2.5, PM1 and ultrafine particles. While PM10 and PM2.5 display a clear relationship, the lack of correlation between PM10 and the coarse fraction of PM10 (PM10–PM2.5) suggests that variation in PM10 is dominated by variance in PM2.5. Given that particles of less than 2.5 μm are suspected to have adverse health effects, increasing the extent of PM2.5 monitoring may improve detection of relationships between air pollution and human health. A lack of correlation between both PM10 and PM2.5 with ultrafine mass concentrations indicates that PM10 and PM2.5 cannot be used as a surrogate for ultrafine mass concentration. Similarly, ultrafine number concentrations cannot be inferred from mass concentration information determined by the MOUDI.