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Analysis Of Ambient Fine Particulate Matter Pm25 In Pittsburgh Using Time Series Techniques And Meteorology
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Book Synopsis Analysis of Ambient Fine Particulate Matter, PM25̣, in Pittsburgh Using Time-series Techniques and Meteorology by : Galina Zubkova
Download or read book Analysis of Ambient Fine Particulate Matter, PM25̣, in Pittsburgh Using Time-series Techniques and Meteorology written by Galina Zubkova and published by . This book was released on 2003 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis COMPARATIVE EVALUATION OF AMBIENT FINE PARTICULATE MATTER (PM2.5)DATA OBTAINED FROM URBAN AND RURAL MONITORING SITES ALONG THE UPPER OHIO RIVER VALLEY. by : Robinson P. Khosah
Download or read book COMPARATIVE EVALUATION OF AMBIENT FINE PARTICULATE MATTER (PM2.5)DATA OBTAINED FROM URBAN AND RURAL MONITORING SITES ALONG THE UPPER OHIO RIVER VALLEY. written by Robinson P. Khosah and published by . This book was released on 2003 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Technology Systems, Inc. (ATS), with Desert Research Institute (DRI) and Ohio University as subcontractors, was contracted by the NETL in September 1998 to manage the Upper Ohio River Valley Project (UORVP), with a goal of characterizing the ambient fine particulate in this region, including examination of urban/rural variations, correlations between PM{sub 2.5} and gaseous pollutants, and influences of artifacts on PM{sub 2.5} measurements in this region. Two urban and two rural monitoring sites were included in the UORVP. The four sites selected were all part of existing local and/or state air quality programs. One urban site was located in the Lawrenceville section of Pittsburgh, Pennsylvania at an air quality monitoring station operated by the Allegheny County Health Department. A second urban site was collocated at a West Virginia Division of Environmental Protection (WVDEP) monitoring station at the airport in Morgantown, West Virginia. One rural site was collocated with the Pennsylvania Department of Environmental Protection (PADEP) at a former NARSTO-Northeast site near Holbrook, Greene County, Pennsylvania. The other rural site was collocated at a site operated by the Ohio Environmental Protection Agency (OHEPA) and managed by the Ohio State Forestry Division in Gifford State Forest near Athens, Ohio. Analysis of data collected to date show that: (1) the median mass and composition of PM{sub 2.5} are similar for both Lawrenceville and Holbrook, suggesting that the sites are impacted more by the regional than by local effects; (2) there was no significant differences in the particulate trending and levels observed at both sites within seasons; (3) sulfate levels predominate at both sites, and (4) PM{sub 2.5} and PM{sub 10} mass concentration levels are consistently higher in summer than in winter, with intermediate levels being observed in the fall and spring. Data analysis focusing on relating the aerometric measurements to local and regional scale emissions of sources of primary and secondary fine particles using receptor-based air quality models will follow.
Book Synopsis Variability in the Fraction of Ambient Fine Particulate Matter in Indoor Air and Implications for Air Pollution Epidemiology by : Natasha Hodas
Download or read book Variability in the Fraction of Ambient Fine Particulate Matter in Indoor Air and Implications for Air Pollution Epidemiology written by Natasha Hodas and published by . This book was released on 2014 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: Exposure to ambient fine particulate matter (PM2.5) is associated with multiple negative health outcomes. Studies investigating these associations commonly use PM2.5 concentrations measured at outdoor, central-site monitors to estimate exposure. Because people spend the majority of time indoors, however, the variable efficiency with which ambient PM2.5 penetrates and persists indoors is a source of error in epidemiologic analyses. This error generally results in an underestimation of health effects, hampering the detection of associations between ambient PM2.5 exposures and the risk of health outcomes. To reduce this error, practical methods to model indoor concentrations of ambient PM2.5 are needed. This dissertation contributes to exposure science by advancing existing models of residential exposure to ambient PM2.5 and by improving the robustness and accessibility of these tools. First, drivers of variability in the fraction of ambient PM2.5 found indoors (F) are identified and the potential for this variability to explain observed heterogeneity in PM-mediated health-effect estimates is explored. Next, a physically-based mass-balance model and modeling tools that account for variability in human activity patterns (e.g. time spent in various indoor and outdoor environments) are used to compute ambient PM2.5 exposures that account for the modification of PM2.5 with outdoor-to-indoor transport in order to explore whether the use of these refined exposure surrogates reduces error and bias in epidemiologic analyses. Subsequently, this outdoor-to-indoor transport model is evaluated and refined using measured indoor and outdoor PM2.5 concentrations and air exchange rates, providing a practical and robust tool for reducing exposure misclassification in epidemiologic studies. Finally, the volatility basis set is used for the first time to study shifts in the gas-particle partitioning of ambient organics with transport indoors. This dissertation provides guidance regarding measurements and data most critically needed to facilitate the prediction of refined exposure surrogates in large epidemiological studies and, thus, informs the design of future sampling campaigns and epidemiologic studies. It enables a better accounting of ambient particle penetration into and persistence in the indoor environment and constitutes an important advancement in the efforts to reduce exposure error in epidemiologic studies and to elucidate relationships between PM2.5 exposure and adverse health outcomes.
Book Synopsis Air-quality Modeling and Source-apportionment of Fine Particulate Matter: Implications and Applications in Time-series Health Studies by : Amit Marmur
Download or read book Air-quality Modeling and Source-apportionment of Fine Particulate Matter: Implications and Applications in Time-series Health Studies written by Amit Marmur and published by . This book was released on 2006 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: Air-quality modeling tools may be useful in such investigations of the health effects of air-pollution and PM2.5 specifically. Emissions-based three-dimensional air quality models may introduce several benefits when applied in epidemiologic studies, such as improved spatial representativeness and availability/continuity of data, as well as information on source impacts. Receptor-based models are a common tool for apportioning of ambient levels of pollutants among the major contributing sources, and can be useful in discerning the relative health impacts of different sources.
Book Synopsis Explore the Link of PM10 with Meteorological Factors and Ambient Air Concentrations of Ozone, CO and NO2 Using Time Series for Cleveland, Ohio by : Charanya Varadarajan
Download or read book Explore the Link of PM10 with Meteorological Factors and Ambient Air Concentrations of Ozone, CO and NO2 Using Time Series for Cleveland, Ohio written by Charanya Varadarajan and published by . This book was released on 2004 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: Major urban areas are finding air quality problems such as, high concentrations of particulate matter. Particles less than 10 microns (PM10) in diameter are targeted because these small particles can easily penetrate into the deepest regions of the lungs. Epidemiological studies strongly confirm the hazards of breathing fine particles at concentrations typically found in ambient air in U.S. cities. The stringent standards and the rigorous measures taken to reduce the levels of particulates since 1996, has resulted in a considerable decrease in the pollutant concentration. Ambient concentrations of PM10 collected in Cleveland, Ohio are analyzed to study the behavior of PM10 and factors that affect variation in PM10. Two approaches of time series modeling technique - KZ Filter and Box Jenkins Transfer Function are used to analyze the data. The trend models developed from the KZ filter process and the cross correlations from the Box Jenkins procedure are used to establish the relationships of PM10 with meteorological variables as well as the ambient concentrations of the photochemical contaminants (ozone (O3), nitrogen dioxide (NO2) and Carbon monoxide (CO)). The models were developed for both the hourly and daily average values of PM10. The robustness of the KZ models was determined using three evaluation parameters while the transfer function models were tested for their convergence with their residual plots. The typical inputs for the hourly KZ filter models included hourly temperature, wind speed and ozone concentration. The KZ analysis showed that hourly PM10 has positive association with hourly temperature for most of the periods for the city of Cleveland. Hourly PM10 showed comparatively less relation to ozone and wind speed. It was found that for different periods of a year, different types of models were relevant to explain the behavior of the data. The transfer function procedure for PM10 and the ambient concentrations of ozone, carbon monoxide, and nitrogen dioxide revealed the important lags of each of the variables that need to be considered for forecasting PM10 in Cleveland. The transfer function procedure revealed that there is significant correlation between PM10 and the other gas concentrations. The analysis showed that NO2 in Cleveland is strongly correlated with PM10. CO was more significantly correlated to PM10 during the periods of April to October. Significant relation between next day ozone and current PM10 concentrations were also revealed from this study.
Book Synopsis COMPARATIVE EVALUATION OF AMBIENT FINE PARTICULATE MATTER (PM2.5) DATA OBTAINED FROM URBAN AND RURAL MONITORING SITES ALONG THE UPPER OHIO RIVER VALLEY. by :
Download or read book COMPARATIVE EVALUATION OF AMBIENT FINE PARTICULATE MATTER (PM2.5) DATA OBTAINED FROM URBAN AND RURAL MONITORING SITES ALONG THE UPPER OHIO RIVER VALLEY. written by and published by . This book was released on 2004 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Technology Systems, Inc. (ATS), with Desert Research Institute (DRI) and Ohio University as subcontractors, was contracted by the NETL in September 1998 to manage the Upper Ohio River Valley Project (UORVP), with a goal of characterizing the ambient fine particulate in this region, including examination of urban/rural variations, correlations between PM{sub 2.5} and gaseous pollutants, and influences of artifacts on PM{sub 2.5} measurements in this region. Two urban and two rural monitoring sites were included in the UORVP. The four sites selected were all part of existing local and/or state air quality programs. One urban site was located in the Lawrenceville section of Pittsburgh, Pennsylvania at an air quality monitoring station operated by the Allegheny County Health Department. A second urban site was collocated at a West Virginia Division of Environmental Protection (WVDEP) monitoring station at the airport in Morgantown, West Virginia. One rural site was collocated with the Pennsylvania Department of Environmental Protection (PADEP) at a former NARSTO-Northeast site near Holbrook, Greene County, Pennsylvania. The other rural site was collocated at a site operated by the Ohio Environmental Protection Agency (OHEPA) and managed by the Ohio State Forestry Division in Gifford State Forest near Athens, Ohio. Previous Semi-Annual Technical Progress Reports presented the following: (1) the median mass and composition of PM{sub 2.5} are similar for both Lawrenceville and Holbrook, suggesting that the sites are impacted more by the regional than by local effects; (2) there was no significant differences in the particulate trending and levels observed at both sites within seasons; (3) sulfate levels predominate at both sites and (4) PM{sub 2.5} and PM10 mass concentration levels are consistently higher in summer than in winter, with intermediate levels being observed in the fall and spring. Analyses of data conducted during the period from April 1, 2003 through September 30, 2003 are presented in this Semi-Annual Technical Progress Report. Report Revision No. 1 includes the additions or removals of text presented in the previous version of this report.
Book Synopsis Assessing Correlation Between PM2.5 and Meteorological Variables and Projecting the Impact of Climate Change on PM2.5 by : Xiaojun Su
Download or read book Assessing Correlation Between PM2.5 and Meteorological Variables and Projecting the Impact of Climate Change on PM2.5 written by Xiaojun Su and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study builds the Statistical Downscaling Model (SDSM), coupled with the Artificial Neural Network (ANN), to identify meteorological variables that show strong influence on the concentrations of fine particulate matter (PM2.5) and to project future PM2.5 concentrations using global climate model in IPCC Fifth Assessment Report (AR5). Toronto and Sarnia, Canada are chosen to study the effects of meteorological influence and climate change on PM2.5, as a comparison of metropolitan and industrial cities. Higher PM2.5 in Summer are detected which is affected by the long range transport of pollutants. Seasonal models are built using ANN in both cities to study the influential predictors in each season, which perform better than annual models with a 10-15% increase in R2 value. The SDSM model projects future PM2.5 under the assumption of constant emissions. Results show that the impact of climate change on PM2.5 is relatively small due to the cancellation of opposite changes caused by predictors.
Book Synopsis Factors Influencing Ambient Particulate Matter by : Kanan Patel
Download or read book Factors Influencing Ambient Particulate Matter written by Kanan Patel and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Long term exposure to particulate matter (PM) has been linked to an increase in mortality and cardiorespiratory diseases. In addition, PM affects Earth’s radiative balance, and is one of the main sources of uncertainty in climate change predictions. Hence, it is imperative to understand PM composition and concentrations and the factors contributing to their variability. Different parts of the world experience different levels of air pollution, due to an interplay between various factors including sources, meteorological factors, and chemical transformations. PM can either be directly emitted into the atmosphere (primary) or can be generated as result of oxidation of gas-phase precursors leading to the formation and partitioning of low volatility products to the particle phase (secondary). The nature, sources and dynamics of PM can be estimated by combining ambient field measurements with receptor modeling, machine learning and statistical analysis tools. The objective of my thesis is to understand the factors influencing PM concentration and composition in different environments. In chapter 2, I have reported the results of the measurements in Austin, Texas, one of the fastest growing metropolitan cities in the U.S. I used several modeling and data analysis tools to understand the sources and formation of particulate matter in Austin including positive matrix factorization (PMF), the Extended Aerosol Thermodynamics Model (E-AIM) and air back trajectory analysis using HYSPLIT. Through my analysis, I demonstrated that photochemistry is an important factor in governing PM composition in Austin. We observed rapid photochemical processing of traffic emissions, H2SO4-driven new particle formation (NPF) events, production of organic nitrate, and daytime peaks in the locally formed oxidized organic aerosol during the summer period. My analysis also suggested that SO2 emissions from cement kilns may be the main source of particulate sulfate observed at this receptor site, pointing toward the need for measurements at the source to investigate this further. This chapter has been published in ACS Earth and Space Chemistry. Meanwhile, Delhi (India) is the most polluted megacity in the world and routinely experiences extreme pollution episodes. Our group is one of the first in the world to measure long term PM composition at high time resolution in the city. As part of the Delhi Aerosol Supersite (DAS) study, we have recorded over five years of near-continuous PM composition to understand inter-seasonal as well as inter-annual variability in the PM concentrations and the factors influencing them. I have studied specific “special” events which have implications for policy decisions. In chapter 3, I have investigated the factors influencing high PM concentrations observed during the autumn (~Sep – Nov) season which experiences some of the most extreme pollution episodes observed anywhere in the world. I combined our measurements with data obtained from regulatory monitoring sites (CO, NOx, PM2.5) to gain insights from the temporal trends of the pollutants and to demonstrate the differences between autumn and winter, which also experiences high concentrations. I incorporated receptor models and non-parametric wind regression to understand the nature and sources of PM during this period. Further, I used meteorological data such as temperature, planetary boundary layer height, wind speed/direction and relative humidity to understand their impact on PM using statistical hypothesis testing. Using these tools, I demonstrated the influence of regional agricultural burning (from the neighboring states) and fireworks during the festival of Diwali on PM during this season. Overall, my analysis provided detailed insights into the sources and dynamics of PM during one of the most polluted seasons in Delhi (and in the world) and provided a direction for future studies in the region. This chapter has also been published in ACS Earth and Space Chemistry. In chapter 4, I have investigated the impact of COVID-19 lock-down on Delhi's air quality by combining PM and gas phase data of over four years with robust statistical analysis, including the method of “robust differences” to account for seasonal variability in the pollutant concentrations. My analysis suggests that future large-scale modification of activity restrictions in Delhi may impact the primary pollutants (NOx, CO, black carbon) more than the secondary pollutants, emphasizing the fundamental importance of secondary or regional pollutants on air quality in Delhi. I showed that overall, future strict activity reductions may lead to only a moderate reduction in PM1, reflective of complex PM1 chemistry and the need for integrative, multiscale, and multisectoral policies to address the major air pollution challenge in Delhi. This chapter has been published in ACS Environmental Science & Technology Letters. Because of the interplay between sources and meteorology in Delhi, in chapter 5 I have developed machine learning models incorporating random forest regression that estimate the concentrations of PM1 and its constituents by using meteorology and emission proxies. I have demonstrated the applicability of these models to capture temporal variability of the PM1 species, to understand the influence of individual factors via sensitivity analyses, and to separate impacts of the COVID-19 lockdowns and associated activity restrictions from impacts of other factors. Overall, these models provide new insights into the factors influencing ambient PM1 in New Delhi, India, demonstrating the power of machine learning models in atmospheric science applications. This chapter will be submitted to Aerosol Science and Technology. My research has advanced our understanding about PM formation and processing in different environments. These novel measurements and analyses will help guide future studies aimed at understanding and improving ambient air quality in these regions. Furthermore, the results of my scientific analyses may help guide policy decisions aimed at reducing PM levels in the atmosphere, thus helping improve the lives of millions of people
Book Synopsis Modeling Spatiotemporal Patterns of PM2.5 at the Sub-Neighborhood Scale Using Low-Cost Sensor Networks by :
Download or read book Modeling Spatiotemporal Patterns of PM2.5 at the Sub-Neighborhood Scale Using Low-Cost Sensor Networks written by and published by . This book was released on 2019 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: Epidemiological research has demonstrated an adverse relationship between fine particulate matter (PM2.5) and human health. While PM2.5 continues to pose a significant global health risk, there is still the need to further characterize exposures at the intra-urban scale. Land use regression is a statistical modeling technique which is used to predict air pollution concentrations at high resolution from a limited number of monitoring sites. However, the existing regulatory monitoring networks are typically not dense enough to apply these techniques. We explored the potential of using low-cost PM2.5 sensor networks to overcome the limitations of the existing regulatory monitoring infrastructure, and identified the need to determine sensor-specific correction factors based on the local PM2.5 source profile. Once calibrated, a land use regression model (R2 = 0.89) was developed using the low-cost sensor network (n ≈ 20), alongside several land use and meteorological variables, to predict daily particulate matter concentrations at a 50 m spatial resolution during a two year period within Portland, Oregon. From this model, we assessed the relative strengths of expected sources and sinks of fine particulate matter, focusing specifically on the role that the urban canopy may play in mitigating PM2.5 exposure. This model showed a modest but observable spatial pattern in PM2.5, but attributed the majority of PM2.5 variation to temporal predictors (e.g. ambient background PM2.5, wind speed, temperature). Neither proxies for traffic-related sources, or vegetation-related sinks were identified as significant predictors of PM2.5. Our research also demonstrated the importance of sensor placement, as a considerably different set of predictors was selected after the inclusion of four additional monitoring sites. Future work will apply this method to four cities with a varying degree of canopy cover to assess differences in intra-urban gradients of PM2.5 and to further characterize the influence of vegetation.
Book Synopsis Statistical Analysis And Modeling Of Pm2.5 Speciation Metals And Their Mixtures by : Boubakari Ibrahimou
Download or read book Statistical Analysis And Modeling Of Pm2.5 Speciation Metals And Their Mixtures written by Boubakari Ibrahimou and published by . This book was released on 2014 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Exposure to fine particulate matter (PM2.5) in the ambient air is associated with various health effects. There is increasing evidence which implicates the central role played by specific chemical components such as heavy metals of PM2.5. Given the fact that humans are exposed to complex mixtures of environmental pollutants such as PM2.5, research efforts are intensifying to study the mixtures composition and the emission sources of ambient PM, and the exposure-related health effects. Factor analysis as well source apportionment models are statistical tools potentially useful for characterizing mixtures in PM2.5. However, classic factor analysis is designed to analyze samples of independent data. To handle (spatio-)temporally correlated PM2.5 data, a Bayesian approach is developed and using source apportionment, a latent factor is converted to a mixture by utilizing loadings to compute mixture coefficients. Additionally there have been intensified efforts in studying the metal composition and variation in ambient PM as well as its association with health outcomes. We use non parametric smoothing methods to study the spatio-temporal patterns and variation of common PM metals and their mixtures. Lastly the risk of low birth weight following exposure to metal mixtures during pregnancy is being investigated.
Book Synopsis The Economic Consequences of Outdoor Air Pollution by : OECD
Download or read book The Economic Consequences of Outdoor Air Pollution written by OECD and published by OECD Publishing. This book was released on 2016-06-09 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report provides a comprehensive assessment of the economic consequences of outdoor air pollution in the coming decades, focusing on the impacts on mortality, morbidity, and changes in crop yields as caused by high concentrations of pollutants.
Book Synopsis Monitoring Particulate Matter with Commodity Hardware by : David Holstius
Download or read book Monitoring Particulate Matter with Commodity Hardware written by David Holstius and published by . This book was released on 2014 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: Health effects attributed to outdoor fine particulate matter (PM2.5) rank it among the risk factors with the highest health burdens in the world, annually accounting for over 3.2 million premature deaths and over 76 million lost disability-adjusted life years. Existing PM2.5 monitoring infrastructure cannot, however, be used to resolve variations in ambient PM2.5 concentrations with adequate spatial and temporal density, or with adequate coverage of human time-activity patterns, such that the needs of modern exposure science and control can be met. Small, inexpensive, and portable devices, relying on newly available off-the-shelf sensors, may facilitate the creation of PM2.5 datasets with improved resolution and coverage, especially if many such devices can be deployed concurrently with low system cost. Datasets generated with such technology could be used to overcome many important problems associated with exposure misclassification in air pollution epidemiology. Chapter 2 presents an epidemiological study of PM2.5 that used data from ambient monitoring stations in the Los Angeles basin to observe a decrease of 6.1 g (95% CI: 3.5, 8.7) in population mean birthweight following in utero exposure to the Southern California wildfires of 2003, but was otherwise limited by the sparsity of the empirical basis for exposure assessment. Chapter 3 demonstrates technical potential for remedying PM2.5 monitoring deficiencies, beginning with the generation of low-cost yet useful estimates of hourly and daily PM2.5 concentrations at a regulatory monitoring site. The context (an urban neighborhood proximate to a major goods-movement corridor) and the method (an off-the-shelf sensor costing approximately USD $10, combined with other low-cost, open-source, readily available hardware) were selected to have special significance among researchers and practitioners affiliated with contemporary communities of practice in public health and citizen science. As operationalized by correlation with 1h data from a Federal Equivalent Method (FEM) [beta]-attenuation data, prototype instruments performed as well as commercially available equipment costing considerably more, and as well as another reference instrument under similar conditions at the same timescale (R2 = 0.6). Correlations were stronger when 24 h integrating times were used instead (R2 = 0.72). Chapter 4 replicates and extends the results of Chapter 3, showing that similar calibrations may be reasonably exchangeable between near-roadway and background monitoring sites. Chapter 4 also employs triplicate sensors to obtain data consistent with near-field (
Book Synopsis Environmental Health Perspectives by :
Download or read book Environmental Health Perspectives written by and published by . This book was released on 1993 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Links Between Air Quality and Economic Growth by : Shanthi Nataraj
Download or read book Links Between Air Quality and Economic Growth written by Shanthi Nataraj and published by Rand Corporation. This book was released on 2013-12-20 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report assesses what evidence exists for the ways in which local air quality could influence local economic growth and how those effects might be relevant to the Pittsburgh region.
Download or read book The Inside Story written by and published by . This book was released on 1993 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Journal of the Air & Waste Management Association by :
Download or read book Journal of the Air & Waste Management Association written by and published by . This book was released on 2008 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Issue Paper on the Human Health Effects of Metals by :
Download or read book Issue Paper on the Human Health Effects of Metals written by and published by . This book was released on 2004 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt:
