Evaluation of factors influencing road dust loadings in a Latin American urban center

Vehicle non-exhaust emissions are a major component of particle matter, including the direct wear of tires, brakes, road, and the resuspension of deposited particles. It is suggested that resuspended PM (RPM) emissions can be at the same magnitude or even larger than combustion emissions in urban centers. Factors affecting RPM can be included in four categories: road characteristics, traffic condition, land use, and meteorology. In order to study and evaluate these influencing factors, road dust less than 10 micrometers (RD10) was collected in 41 sites across Bogotá. The sampling points had diverse characteristics. RD10 levels varied between 1.0 and 45.8 mg/m2 with an average of 8.9 ± 8.4 mg/m2. Lower RD10 values were observed when vegetation density was high, pavement condition good, driving speeds fast and construction activities absent. On the contrary, RD10 increased under heavy-duty traffic influence and dry conditions. Among dust mitigation measures, management of land-use variables could be as important as traffic control and road maintenance.Implications: This study documented for the first time in Latin America dust loadings less than 10 micrometers, information that can be used to estimate resuspended particle matter emissions in the region. The influence of meteorology, traffic characteristics, road condition, and land-use variables was analyzed and quantified. The management of land-use variables could be as important as traffic control and road maintenance for road dust mitigation. Further research interests are discussed

Surrogate indices of insulin resistance using the Matsuda index as reference in adult men—a computational approach

BackgroundOverweight and obesity, high blood pressure, hyperglycemia, hyperlipidemia, and insulin resistance (IR) are strongly associated with non-communicable diseases (NCDs), including type 2 diabetes, cardiovascular disease, stroke, and cancer. Different surrogate indices of IR are derived and validated with the euglycemic–hyperinsulinemic clamp (EHC) test. Thus, using a computational approach to predict IR with Matsuda index as reference, this study aimed to determine the optimal cutoff value and diagnosis accuracy for surrogate indices in non-diabetic young adult men.MethodsA cross-sectional descriptive study was carried out with 93 young men (ages 18–31). Serum levels of glucose and insulin were analyzed in the fasting state and during an oral glucose tolerance test (OGTT). Additionally, clinical, biochemical, hormonal, and anthropometric characteristics and body composition (DEXA) were determined. The computational approach to evaluate the IR diagnostic accuracy and cutoff value using difference parameters was examined, as well as other statistical tools to make the output robust.ResultsThe highest sensitivity and specificity at the optimal cutoff value, respectively, were established for the Homeostasis model assessment of insulin resistance index (HOMA-IR) (0.91; 0.98; 3.40), the Quantitative insulin sensitivity check index (QUICKI) (0.98; 0.96; 0.33), the triglyceride-glucose (TyG)-waist circumference index (TyG-WC) (1.00; 1.00; 427.77), the TyG-body mass index (TyG-BMI) (1.00; 1.00; 132.44), TyG-waist-to-height ratio (TyG-WHtR) (0.98; 1.00; 2.48), waist-to-height ratio (WHtR) (1.00; 1.00; 0.53), waist circumference (WC) (1.00; 1.00; 92.63), body mass index (BMI) (1.00; 1.00; 28.69), total body fat percentage (TFM) (%) (1.00; 1.00; 31.07), android fat (AF) (%) (1.00; 0.98; 40.33), lipid accumulation product (LAP) (0.84; 1.00; 45.49), leptin (0.91; 1.00; 16.08), leptin/adiponectin ratio (LAR) (0.84; 1.00; 1.17), and fasting insulin (0.91; 0.98; 16.01).ConclusionsThe computational approach was used to determine the diagnosis accuracy and the optimal cutoff value for IR to be used in preventive healthcare

A new database of on-road vehicle emission factors for Colombia: A case study of bogota

Mobile sources contribute directly or indirectly with most of the atmospheric emissions in Colombian cities. Quantification of mobile source emissions rely on emission factors (EF) and vehicle activity. Estimated EF before 2010 may not reflect the reduction of sulfur content in diesel and the renovation and deterioration of passenger vehicles, thus, emission levels may be over or under estimated. To account for these changes, the MOVES model in Bogota and obtained a new database of on-road vehicle emission factors was implemented. Local information of activity rates, speed profiles, vehicle population distribution and age, meteorology and fuel composition was used. Emissions were estimated with these new set of EF and compared with previous inventories. Large reductions in SO (-87%), CO (-65%), and VOC (-62%) emissions from mobiles sources and lower reductions in NO (-20%) were observed. Other pollutants, e.g., PM (+15%) and CO (+28%) reported increases. A new database of onroad vehicle emission factors for Bogota, which can be applied in other Colombian cities in the absence of local data, is provided. 2 x 2.5

PM\u3csub\u3e10\u3c/sub\u3e characterization and source apportionment at two residential areas in Bogota

Bogota is the largest city in Colombia and is considered one of the most ones polluted in Latin America. The annual average PM concentration in the city is 55 μg/m , being as high as 90 μg/m in the western region of the city. In this study, two sites in the western region were selected to assess the PM contribution from different sources. Two sets of fifty five 24-hour PM samples were taken at each site on quartz and Teflon filters. Chemical analysis of these samples was conducted to determine the ion, metal, and organic and elemental carbon concentrations. Ionic balance and mass closure were performed to check the consistency of chemical analysis. Positive Matrix Factorization (PMF) was then applied to determine the source contributions. Mobile sources and fugitive windblown dust were found to be the most significant sources of PM at both sites. An ion factor and a secondary aerosol source factor were identified at one site, whereas industry-related factors were identified at the other site, as expected in an area with a high density of small and medium industrial facilities. While it is true that source apportionment studies have been conducted worldwide, this is the first time that the Positive Matrix Factorization (PMF) model is applied in Bogota using full PM chemical speciation data, including carbonaceous materials, metals and ions. It is also the first time that a receptor model is applied simultaneously in two sites of the city. We aim that the results from this study will support environmental authorities in designing effective air pollution abatement measures in the city. © Author(s) 2012. 10 10 10 10 10 3

Evaluation of fire weather forecasts using PM\u3csub\u3e2.5\u3c/sub\u3e sensitivity analysis

Fire weather forecasts are used by land and wildlife managers to determine when meteorological and fuel conditions are suitable to conduct prescribed burning. In this work, we investigate the sensitivity of ambient PM to various fire and meteorological variables in a spatial setting that is typical for the southeastern US, where prescribed fires are the single largest source of fine particulate matter. We use the method of principle components regression to estimate sensitivity of PM , measured at a monitoring site in Jacksonville, NC (JVL), to fire data and observed and forecast meteorological variables. Fire data were gathered from prescribed fire activity used for ecological management at Marine Corps Base Camp Lejeune, extending 10–50 km south from the PM monitor. Principal components analysis (PCA) was run on 10 data sets that included acres of prescribed burning activity (PB) along with meteorological forecast data alone or in combination with observations. For each data set, observed PM (unitless) was regressed against PCA scores from the first seven principal components (explaining at least 80% of total variance). PM showed significant sensitivity to PB: 3.6 ± 2.2 μg m per 1000 acres burned at the investigated distance scale of ∼10–50 km. Applying this sensitivity to the available activity data revealed a prescribed burning source contribution to measured PM of up to 25% on a given day. PM showed a positive sensitivity to relative humidity and temperature, and was also sensitive to wind direction, indicating the capture of more regional aerosol processing and transport effects. As expected, PM had a negative sensitivity to dispersive variables but only showed a statistically significant negative sensitivity to ventilation rate, highlighting the importance of this parameter to fire managers. A positive sensitivity to forecast precipitation was found, consistent with the practice of conducting prescribed burning on days when rain can naturally extinguish fires. Perhaps most importantly for land managers, our analysis suggests that instead of relying on the forecasts from a day before, prescribed burning decisions should be based on the forecasts released the morning of the burn when possible, since these data were more stable and yielded more statistically robust results. 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 −

Revising the use of potassium (K) in the source apportionment of PM\u3csub\u3e2.5\u3c/sub\u3e

Elemental potassium has been extensively used as an indicator of biomass burning in the source apportionment of PM . We present a method to estimate the fraction of potassium associated with biomass burning (K ) based on a linear regression with iron that can be applied at any site where PM chemical speciation is available. The estimated fraction has a significantly greater correlation with levoglucosan (R =0.63), an organic tracer of biomass burning, than total potassium (R =0.39). We explore temporal and spatial variability of K over a period of six years in the Atlanta area. K is larger in spring when biomass burning activity is more prevalent and during weekends due to the use of fireplaces in winter and outdoor charcoal cooking in summer. K is the predominate form of potassium for the rural site in this study. The use of K in a receptor model results in a lower fraction of PM apportioned to biomass burning and a greater fraction to mobile sources when compared to the use of total K. Results suggest that K is a good indicator of biomass burning. © Author(s) 2012. 2.5 b 2.5 b b b b 2.5 b 2

Revising the use of potassium (K) in the source apportionment of PM2.5

AbstractElemental potassium has been extensively used as an indicator of biomass burning in the source apportionment of PM2.5. We present a method to estimate the fraction of potassium associated with biomass burning (Kb) based on a linear regression with iron that can be applied at any site where PM2.5 chemical speciation is available. The estimated fraction has a significantly greater correlation with levoglucosan (R2=0.63), an organic tracer of biomass burning, than total potassium (R2=0.39). We explore temporal and spatial variability of Kb over a period of six years in the Atlanta area. Kb is larger in spring when biomass burning activity is more prevalent and during weekends due to the use of fireplaces in winter and outdoor charcoal cooking in summer. Kb is the predominate form of potassium for the rural site in this study. The use of Kb in a receptor model results in a lower fraction of PM2.5 apportioned to biomass burning and a greater fraction to mobile sources when compared to the use of total K. Results suggest that Kb is a good indicator of biomass burning

Ensemble-trained source apportionment of fine particulate matter and method uncertainty analysis

An ensemble-based approach is applied to better estimate source impacts on fine particulate matter (PM ) and quantify uncertainties in various source apportionment (SA) methods. The approach combines source impacts from applications of four individual SA methods: three receptor-based models and one chemical transport model (CTM). Receptor models used are the chemical mass balance methods CMB-LGO (Chemical Mass Balance-Lipschitz global optimizer) and CMB-MM (molecular markers) as well as a factor analytic method, Positive Matrix Factorization (PMF). The CTM used is the Community Multiscale Air Quality (CMAQ) model. New source impact estimates and uncertainties in these estimates are calculated in a two-step process. First, an ensemble average is calculated for each source category using results from applying the four individual SA methods. The root mean square error (RMSE) between each method with respect to the average is calculated for each source category; the RMSE is then taken to be the updated uncertainty for each individual SA method. Second, these new uncertainties are used to re-estimate ensemble source impacts and uncertainties. The approach is applied to data from daily PM measurements at the Atlanta, GA, Jefferson Street (JST) site in July 2001 and January 2002. The procedure provides updated uncertainties for the individual SA methods that are calculated in a consistent way across methods. Overall, the ensemble has lower relative uncertainties as compared to the individual SA methods. Calculated CMB-LGO uncertainties tend to decrease from initial estimates, while PMF and CMB-MM uncertainties increase. Estimated CMAQ source impact uncertainties are comparable to other SA methods for gasoline vehicles and SOC but are larger than other methods for other sources. In addition to providing improved estimates of source impact uncertainties, the ensemble estimates do not have unrealistic extremes as compared to individual SA methods and avoids zero impact days. © 2012 Elsevier Ltd. 2.5 2.

Comparison of SOC estimates and uncertainties from aerosol chemical composition and gas phase data in Atlanta

In the Southeastern US, organic carbon (OC) comprises about 30% of the PM mass. A large fraction of OC is estimated to be of secondary origin. Long-term estimates of SOC and uncertainties are necessary in the evaluation of air quality policy effectiveness and epidemiologic studies. Four methods to estimate secondary organic carbon (SOC) and respective uncertainties are compared utilizing PM chemical composition and gas phase data available in Atlanta from 1999 to 2007. The elemental carbon (EC) tracer and the regression methods, which rely on the use of tracer species of primary and secondary OC formation, provided intermediate estimates of SOC as 30% of OC. The other two methods, chemical mass balance (CMB) and positive matrix factorization (PMF) solve mass balance equations to estimate primary and secondary fractions based on source profiles and statistically-derived common factors, respectively. CMB had the highest estimate of SOC (46% of OC) while PMF led to the lowest (26% of OC). The comparison of SOC uncertainties, estimated based on propagation of errors, led to the regression method having the lowest uncertainty among the four methods. We compared the estimates with the water soluble fraction of the OC, which has been suggested as a surrogate of SOC when biomass burning is negligible, and found a similar trend with SOC estimates from the regression method. The regression method also showed the strongest correlation with daily SOC estimates from CMB using molecular markers. The regression method shows advantages over the other methods in the calculation of a long-term series of SOC estimates. © 2010 Elsevier Ltd. 2.5 2.

Bayesian-based ensemble source apportionment of PM\u3csub\u3e2.5\u3c/sub\u3e

A Bayesian source apportionment (SA) method is developed to provide source impact estimates and associated uncertainties. Bayesian-based ensemble averaging of multiple models provides new source profiles for use in a chemical mass balance (CMB) SA of fine particulate matter (PM ). The approach estimates source impacts and their uncertainties by using a short-term application of four individual SA methods: three receptor-based models and one chemical transport model. The method is used to estimate two seasonal distributions of source profiles that are used in SA for a long-term PM data set. For each day in a long-term PM data set, 10 source profiles are sampled from these distributions and used in a CMB application, resulting in 10 SA results for each day. This formulation results in a distribution of daily source impacts rather than a single value. The average and standard deviation of the distribution are used as the final estimate of source impact and a measure of uncertainty, respectively. The Bayesian-based source impacts for biomass burning correlate better with observed levoglucosan (R = 0.66) and water-soluble potassium (R = 0.63) than source impacts estimated using more traditional methods and more closely agrees with observed total mass. The Bayesian approach also captures the expected seasonal variation of biomass burning and secondary impacts and results in fewer days with sources having zero impact. Sensitivity analysis found that using non-informative prior weighting performed better than using weighting based on method-derived uncertainties. This approach can be applied to long-term data sets from speciation network sites of the United States Environmental Protection Agency (U.S. EPA). In addition to providing results that are more consistent with independent observations and known emission sources being present, the distributions of source impacts can be used in epidemiologic analyses to estimate uncertainties associated with the SA results. © 2013 American Chemical Society. 2.5 2.5 2.5 2