Mohammad Arhami

Characterization of PAHs and metals in indoor/outdoor PM10/PM2.5/PM1 in a retirement home and a school dormitory.

In the present work, we investigated the characteristics of polycyclic aromatic hydrocarbons (PAHs) and metal(loid)s in indoor/outdoor PM10, PM2.5, and PM1 in a retirement home and a school dormitory in Tehran from May 2012 to May 2013. The results indicated that the annual levels of indoor and outdoor PM10 and PM2.5 were much higher than the guidelines issued by the World Health Organization (WHO). The most abundant detected metal(loid)s in PM were Si, Fe, Zn, Al, and Pb. We found higher percentages of metal(loid)s in smaller size fractions of PM. Additionally, the results showed that the total PAHs (ƩPAHs) bound to PM were predominantly (83-88%) found in PM2.5, which can penetrate deep into the alveolar regions of the lungs. In general, carcinogenic PAHs accounted for 40-47% of the total PAHs concentrations; furthermore, the smaller the particle size, the higher the percentage of carcinogenic PAHs. The percentages of trace metal(loid)s and carcinogenic PAHs in PM2.5 mass were almost twice as high as those in PM10. This can most likely be responsible for the fact that PM2.5 can cause more adverse health effects than PM10 can. The average BaP-equivalent carcinogenic (BaP-TEQ) levels both indoors and outdoors considerably exceeded the maximum permissible risk level of 1 ng/m(3) of BaP. The enrichment factors and diagnostic ratios indicated that combustion-related anthropogenic sources, such as gasoline- and diesel-fueled vehicles as well as natural gas combustion, were the major sources of PAHs and trace metal(loid)s bound to PM.

Organic compound characterization and source apportionment of indoor and outdoor quasi-ultrafine particulate matter in retirement homes of the Los Angeles Basin

UNLABELLEDnQuasi-ultrafine (quasi-UF) particulate matter (PM(0.25)) and its components were measured in indoor and outdoor environments at four retirement communities in Los Angeles Basin, California, as part of the Cardiovascular Health and Air Pollution Study (CHAPS). The present paper focuses on the characterization of the sources, organic constituents and indoor and outdoor relationships of quasi-UF PM. The average indoor/outdoor ratios of most of the measured polycyclic aromatic hydrocarbons (PAHs), hopanes, and steranes were close to or slightly lower than 1, and the corresponding indoor-outdoor correlation coefficients (R) were always positive and, for the most part, moderately strong (median R was 0.60 for PAHs and 0.74 for hopanes and steranes). This may reflect the possible impact of outdoor sources on indoor PAHs, hopanes, and steranes. Conversely, indoor n-alkanes and n-alkanoic acids were likely to be influenced by indoor sources. A chemical mass balance model was applied to both indoor and outdoor speciated chemical measurements of quasi-UF PM. Among all apportioned sources of both indoor and outdoor particles, vehicular emissions was the one contributing the most to the PM(0.25) mass concentration measured at all sites (24-47% on average).nnnPRACTICAL IMPLICATIONSnAlthough people (particularly the elderly retirees of our study) generally spend most of their time indoors, a major portion of the PM(0.25) particles they are exposed to comes from outdoor mobile sources. This is important because, an earlier investigation, also conducted within the Cardiovascular Health and Air Pollution Study (CHAPS), showed that indoor-infiltrated particles from mobile sources are more strongly correlated with adverse health effects observed in the elderly subjects living in the studied retirement communities compared with other particles found indoors (Delfino et al., 2008).

Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations.

Recent progress in developing artificial neural network (ANN) metamodels has paved the way for reliable use of these models in the prediction of air pollutant concentrations in urban atmosphere. However, improvement of prediction performance, proper selection of input parameters and model architecture, and quantification of model uncertainties remain key challenges to their practical use. This study has three main objectives: to select an ensemble of input parameters for ANN metamodels consisting of meteorological variables that are predictable by conventional weather forecast models and variables that properly describe the complex nature of pollutant source conditions in a major city, to optimize the ANN models to achieve the most accurate hourly prediction for a case study (city of Tehran), and to examine a methodology to analyze uncertainties based on ANN and Monte Carlo simulations (MCS). In the current study, the ANNs were constructed to predict criteria pollutants of nitrogen oxides (NOx), nitrogen dioxide (NO2), nitrogen monoxide (NO), ozone (O3), carbon monoxide (CO), and particulate matter with aerodynamic diameter of less than 10xa0μm (PM10) in Tehran based on the data collected at a monitoring station in the densely populated central area of the city. The best combination of input variables was comprehensively investigated taking into account the predictability of meteorological input variables and the study of model performance, correlation coefficients, and spectral analysis. Among numerous meteorological variables, wind speed, air temperature, relative humidity and wind direction were chosen as input variables for the ANN models. The complex nature of pollutant source conditions was reflected through the use of hour of the day and month of the year as input variables and the development of different models for each day of the week. After that, ANN models were constructed and validated, and a methodology of computing prediction intervals (PI) and probability of exceeding air quality thresholds was developed by combining ANNs and MCSs based on Latin Hypercube Sampling (LHS). The results showed that proper ANN models can be used as reliable metamodels for the prediction of hourly air pollutants in urban environments. High correlations were obtained with R2 of more than 0.82 between modeled and observed hourly pollutant levels for CO, NOx, NO2, NO, and PM10. However, predicted O3 levels were less accurate. The combined use of ANNs and MCSs seems very promising in analyzing air pollution prediction uncertainties. Replacing deterministic predictions with probabilistic PIs can enhance the reliability of ANN models and provide a means of quantifying prediction uncertainties.

Evaluating near highway air pollutant levels and estimating emission factors: Case study of Tehran, Iran.

A field sampling campaign was implemented to evaluate the variation in air pollutants levels near a highway in Tehran, Iran (Hemmat highway). The field measurements were used to estimate road link-based emission factors for average vehicle fleet. These factors were compared with results of an in tunnel measurement campaign (in Resalat tunnel). Roadside and in-tunnel measurements of carbon monoxide (CO) and size-fractionated particulate matter (PM) were conducted during the field campaign. The concentration gradient diagrams showed exponential decay, which represented a substantial decay, more than 50-80%, in air pollutants level in a distance between 100 and 150meters (m) of the highway. The changes in particle size distribution by distancing from highway were also captured and evaluated. The results showed particle size distribution shifted to larger size particles by distancing from highway. The empirical emission factors were obtained by using the roadside and in tunnel measurements with a hypothetical box model, floating machine model, CALINE4, CT-EMFAC or COPERT. Average CO emission factors were estimated to be in a range of 4 to 12g/km, and those of PM10 were 0.1 to 0.2g/km, depending on traffic conditions. Variations of these emission factors under real working condition with speeds were determined.

Estimating ground-level PM10 using satellite remote sensing and ground-based meteorological measurements over Tehran.

Background and methodologyMeasurements by satellite remote sensing were combined with ground-based meteorological measurements to estimate ground-level PM10. Aerosol optical depth (AOD) by both MODIS and MISR were utilized to develop several statistical models including linear and non-linear multi-regression models. These models were examined for estimating PM10 measured at the air quality stations in Tehran, Iran, during 2009-2010. Significant issues are associated with airborne particulate matter in this city. Moreover, the performances of the constructed models during the Middle Eastern dust intrusions were examined.ResultsIn general, non-linear multi-regression models outperformed the linear models. The developed models using MISR AOD generally resulted in better estimate of ground-level PM10 compared to models using MODIS AOD. Consequently, among all the constructed models, results of non-linear multi-regression models utilizing MISR AOD acquired the highest correlation with ground level measurements (R2 of up to 0.55). The possibility of developing a single model over all the stations was examined. As expected, the results were depreciated, while nonlinear MISR model repeatedly showed the best performance being able to explain up to 38% of the PM10 variability.ConclusionsGenerally, the models didnt competently reflect wide temporal concentration variations, particularly due to the elevated levels during the dust episodes. Overall, using non-linear multi-regression model incorporating both remote sensing and ground-based meteorological measurements showed a rather optimistic prospective in estimating ground-level PM for the studied area. However, more studies by applying other statistical models and utilizing more parameters are required to increase the model accuracies.

Short-term effects of particle size fractions on circulating biomarkers of inflammation in a panel of elderly subjects and healthy young adults

Systemic inflammation biomarkers have been associated with risk of cardiovascular morbidity and mortality. We aimed to clarify associations of acute exposure to particulate matter (PM10 (PMxa0<xa010xa0μm), PM2.5-10 (PM 2.5-10xa0μm), PM2.5 (PMxa0<xa02.5xa0μm), PM1-2.5 (PM 1-2.5xa0μm), and PM1 (PMxa0<xa01xa0μm)) with systemic inflammation using panels of elderly subjects and healthy young adults. We followed a panel of 44 nonsmoking elderly subjects living in a retirement home and a panel of 40 healthy young adults living in a school dormitory in Tehran city, Iran from May 2012 to May 2013. Blood biomarkers were measured one every 7-8 weeks and included white blood cells (WBC), high sensitive C-reactive protein (hsCRP), tumor necrosis factor-soluble receptor-II (sTNF-RII), interleukin-6 (IL-6), and von Willebrand factor (vWF). We measured hourly indoor and outdoor exposure to PM10, PM2.5-10, PM2.5, PM1-2.5, and PM1 mass concentration to derive weighted averages of personal exposure based on simultaneously collected time-activity data. The random intercept linear mixed effects model was used for data analysis. We observed significant positive associations for WBC and IL-6 with exposure to PM10, PM2.5-10, PM2.5, PM1-2.5, and PM1; sTNF-RII with PM2.5, PM1-2.5, and PM1; hsCRP with PM2.5 and PM1; and vWF with PM10 and PM2.5-10, PM2.5, and PM1-2.5 mass concentration in elderly subjects from the current-day and multiday averages. For healthy young adults, we found significant positive associations for WBC and IL-6 with exposure to PM10, PM2.5-10, PM2.5, and PM1-2.5, but no with PM1. The results showed that increase of hsCRP, sTNF-RII, and vWF were not significantly associated with any of the PM sizes investigated in the healthy young subjects. Our results provided some evidence that short-term exposure to PM10, PM2.5-10, PM2.5, PM1-2.5, and PM1 was associated with inflammation and coagulation blood markers, but associations were depended on PM size and also differed across the various time lag.

Impact of Middle Eastern dust sources on PM10 in Iran: Highlighting the impact of Tigris-Euphrates basin sources and Lake Urmia desiccation

Contribution of different Middle Eastern dust origins to PM10 (PM with aerodynamic diameters less than 10u2009µm) levels in several receptor large cities in Iran was investigated. Initially, the major regional dust episodes were determined through statistical analysis of recorded PM levels at air quality stations and verified using satellite images. The particles dispersion was simulated by Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) to regenerate PM10 during the dust episodes. The accuracy of the modeled results was rather convincing, with an average squared correlation coefficient (R2) of 0.7 (maxu2009=u20090.95). Consequently, the contributions of different dust sources to the observed concentrations were determined. Basin of Tigris-Euphrates Rivers encompasses active dust sources with significant rate of emission due to fluvial deposits. The sources in this basin with approximately 70–95% contribution, by far, had the most influence on PM10 levels at the receptor cities. In a finer resolution, northern and central parts of Iraq had the most influence on PM10 level during the dust episodes. Effect of probable improvement or deterioration of the current dust origin conditions on PM10 levels was analyzed by performing a sensitivity analysis through varying threshold friction velocities. The results demonstrated that 10% increase or decrease in threshold friction velocities of major dust sources could lead to average of 51% decrease or 77% increase in the receptor cities PM10, respectively. Finally, effects of Lake Urmia desiccation, as a new hydrological prospect dust origin were analyzed. The predicted dust from the prospective dried lake bed could result in ~u200930–60% increase in PM10 of nearby cities during the studied dust episodes.

Seasonal trends in the composition and sources of PM2.5 and carbonaceous aerosol in Tehran, Iran

Currently PM2.5 is a major air pollution concern in Tehran, Iran due to frequent high levels and possible adverse impacts. In this study, which is the first of its kind to take place in Tehran, composition and sources of PM2.5 and carbonaceous aerosol were determined, and their seasonal trends were studied. In this regard, fine PM samples were collected every six days at a residential station for one year and the chemical constituents including organic marker species, metals, and ions were analyzed by chemical analysis. The source apportionment was performed using organic molecular marker-based CMB receptor modeling. Carbonaceous compounds were the major contributors to fine particulate mass in Tehran, as OC and EC together comprised on average 29% of PM2.5 mass. Major portions of OC in Tehran were water insoluble and are mainly attributed to primary sources. Higher levels of several PAHs, which are organic tracers of incomplete combustion, and hopanes and steranes as organic tracers of mobile sources were obtained in cold months and compared to the warm months. The major contributing source to particulate OC was identified as vehicles, which contributed about 72% of measured OC. Among mobile sources, gasoline-fueled vehicles had the highest impact with a mean contribution of 48% to the measured OC. Mobile sources also were the largest contributor to total PM2.5 (40%), followed by dust (24%) and sulfate (11%). In addition to primary emissions, mobile sources also directly and indirectly played an important role in another 27% of fine particulate mass (secondary organics and ions), which highlights the impact of vehicles in Tehran. Our results highlighted and quantified the role of motor vehicles in fine PM production, particularly during winter time. The results of this study could be used to set more effective regulations and control strategies particularly upon mobile sources.