Dataset Information

Intra-urban spatial variability and uncertainty assessment of PM2.5 sources based on carbonaceous species.

ABSTRACT: To identify the sources of PM2.5 - bound carbonaceous species and examine the spatial variability of source contributions in the Denver metropolitan area, positive matrix factorization (PMF) was applied to one year of every sixth day ambient PM2.5 compositional data, including elemental carbon (EC), organic carbon (OC), and 32 organic molecular markers, from four sites (two residential and two near-traffic). Statistics (median, inner quantiles and 5th - 95th percentiles range) of factor contributions, expressed as reconstructed carbonaceous mass (EC + OC), were estimated from PMF solutions of replicate datasets generated by using a stationary block bootstrap technique. A seven-factor solution was resolved for a set of data pooled across the four sites, as it gave the most interpretable results and had the highest rate of neural network factor matching (76.9%). Identified factors were primarily associated with high plant wax, summertime emission, diesel vehicle emission, fossil fuel combustion, motor vehicle emission, lubricating oil combustion and wood burning. Pearson correlation coefficients (r) and coefficients of divergence (COD) were used to assess spatial variability of factor contributions. The summertime emission factor exhibited the highest spatial correlation (r = 0.74 - 0.88) and lowest CODs (0.32 - 0.38) among all resolved factors; while the three traffic dominated factors (diesel vehicle emission, motor vehicle emission and lubricating oil combustion) showed lower correlations (r = 0.47 - 0.55) and higher CODs (0.41 - 0.53) on average. Average total EC and OC mass were apportioned to each factor and showed a similar distribution across the four sites. Modeling uncertainties were defined as the 5th - 95th percentile range of the factor contributions derived from valid bootstrap PMF solutions, and were highly correlated with the median factor contribution in each factor (r = 0.77 - 0.98). Source apportionment was also performed on site specific datasets; the results exhibited similar factor profiles and temporal variation in factor contribution as those obtained for the pooled dataset, indicating that the four sites are primarily influenced by similar types of sources. On the other hand, differences were observed in absolute factor contributions between PMF solutions for the pooled versus site-specific datasets, likely due to the large uncertainties in EC and OC factor profiles derived from the site specific datasets with limited numbers of observations.

SUBMITTER: Xie M

PROVIDER: S-EPMC4159203 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

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Intra-urban spatial variability and uncertainty assessment of PM2.5 sources based on carbonaceous species.

Xie Mingjie M Coons Teresa L TL Hemann Joshua G JG Dutton Steven J SJ Milford Jana B JB Peel Jennifer L JL Miller Shelly L SL Kim Sun-Young SY Vedal Sverre S Sheppard Lianne L Hannigan Michael P MP

Atmospheric environment (Oxford, England : 1994) 20121201

To identify the sources of PM2.5 - bound carbonaceous species and examine the spatial variability of source contributions in the Denver metropolitan area, positive matrix factorization (PMF) was applied to one year of every sixth day ambient PM2.5 compositional data, including elemental carbon (EC), organic carbon (OC), and 32 organic molecular markers, from four sites (two residential and two near-traffic). Statistics (median, inner quantiles and 5th - 95th ...[more]

PMID: 25214808

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Project description:The Denver Aerosol Sources and Health (DASH) study was designed to evaluate associations between PM2.5 species and sources and adverse human health effects. The DASH study generated a five-year (2003-2007) time series of daily speciated PM2.5 concentration measurements from a single, special-purpose monitoring site in Denver, CO. To evaluate the ability of this site to adequately represent the short term temporal variability of PM2.5 concentrations in the five county Denver metropolitan area, a one year supplemental set of PM2.5 samples was collected every sixth day at the original DASH monitoring site and concurrently at three additional sites. Two of the four sites, including the original DASH site, were located in residential areas at least 1.9 km from interstate highways. The other two sites were located within 0.3 km of interstate highways. Concentrations of elemental carbon (EC), organic carbon (OC), and 58 organic molecular markers were measured at each site. To assess spatial variability, site pairs were compared using the Pearson correlation coefficient (r) and coefficient of divergence (COD), a statistic that provides information on the degree of uniformity between monitoring sites. Biweekly co-located samples collected from July 2004 to September 2005 were also analyzed and used to estimate the uncertainty associated with sampling and analytical measurement for each species. In general, the two near-highway sites exhibited higher concentrations of EC, OC, polycyclic aromatic hydrocarbons (PAHs), and steranes than did the more residential sites. Lower spatial heterogeneity based on r and COD was inferred for all carbonaceous species after considering their divergence and lack of perfect correlations in co-located samples. Ratio-ratio plots combined with available gasoline- and diesel-powered motor vehicle emissions profiles for the region suggested a greater impact to high molecular weight (HMW) PAHs from diesel-powered vehicles at the near-highway sites and a more uniformly distributed impact to ambient hopanes from gasoline-powered motor vehicles at all four sites.

Project description:Abstract Air pollution levels are uneven within cities, contributing to persistent health disparities between neighborhoods and population sub‐groups. Highly spatially resolved information on pollution levels and disease rates is necessary to characterize inequities in air pollution exposure and related health risks. We leverage recent advances in deriving surface pollution levels from satellite remote sensing and granular data in disease rates for one city, Washington, DC, to assess intra‐urban heterogeneity in fine particulate matter (PM2.5)‐ attributable mortality and morbidity. We estimate PM2.5‐attributable cases of all‐cause mortality, chronic obstructive pulmonary disease, ischemic heart disease, lung cancer, stroke, and asthma emergency department (ED) visits using epidemiologically derived health impact functions. Data inputs include satellite‐derived annual mean surface PM2.5 concentrations; age‐resolved population estimates; and statistical neighborhood‐, zip code‐ and ward‐scale disease counts. We find that PM2.5 concentrations and associated health burdens have decreased in DC between 2000 and 2018, from approximately 240 to 120 cause‐specific deaths and from 40 to 30 asthma ED visits per year (between 2014 and 2018). However, remaining PM2.5‐attributable health risks are unevenly and inequitably distributed across the District. Higher PM2.5‐attributable disease burdens were found in neighborhoods with larger proportions of people of color, lower household income, and lower educational attainment. Our study adds to the growing body of literature documenting the inequity in air pollution exposure levels and pollution health risks between population sub‐groups, and highlights the need for both high‐resolution disease rates and concentration estimates for understanding intra‐urban disparities in air pollution‐related health risks. Key Points Fine particulate matter‐attributable health risks are unevenly and inequitably distributed across Washington, DC Higher PM2.5‐attributable disease burdens are found in neighborhoods with larger proportions of people of color in Washington, DC High‐resolution disease and concentration estimates are needed to understand intra‐urban disparities in air pollution‐related health risks

Project description:The SHERPA tool was used to assess the major pollution sources and the geographical areas impacting on the PM2.5 of the main cities in the Danube and Western Balkans regions. The activity sectors influencing most the PM2.5 levels in the study area are energy production (22%), agriculture (19%), residential combustion (16%) and road transport (7%). The energy production in inefficient coal-fuelled power plants was identified as one of main source of PM2.5 in the Western Balkans. As for the geographical origin of PM2.5, the transboundary pollution is confirmed as the main origin of PM2.5 (44%) in the investigated cities, while the city own emissions and the national sources outside the concerned city impact on average 22% and 15%, respectively. An association was observed between the long-range transport and the impact of agriculture and energy production, while both local urban emissions and long-range transport were associated with the residential sector. A special attention is given in this study to biomass, a renewable source, which use is often promoted in the frame of climate and energy policies. Nevertheless, the combustion of biomass in inefficient small appliances has considerable particulate matter emissions and therefore this type of practice impacts negatively on air quality. Considering that biomass is traditionally used in South-East Europe as fuel for residential heating, the interpretation of the model results was supported with the estimation of biomass burning contributions to PM2.5 obtained with receptor models and data on biomass fuel consumption from the literature. The analysis of the contributions from biomass burning derived from receptor models suggests that biomass burning is the dominant source within the residential heating sector in the studied area and that the emissions from this source are likely underestimated. This study concludes that more effort is needed to improve the estimations of biomass burning emissions and that policies to improve air quality in the cities should involve a geographic context wider than the city level.

Dataset Information

Intra-urban spatial variability and uncertainty assessment of PM2.5 sources based on carbonaceous species.

Publications

Intra-urban spatial variability and uncertainty assessment of PM<sub>2.5</sub> sources based on carbonaceous species.

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