The air quality impact of aviation in future-year emissions scenarios

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2011.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 104-112).The rapid growth of aviation is critical to the world and US economy, and it faces several important challenges among which lie the environmental impacts of aviation on noise, climate and air quality. The first objective of this thesis addresses the requirements of section 753 of the US Energy Policy Act, and entails the quantification of present and future-year regional air quality impacts of US Landing and Take-Off (LTO) aviation emissions. In addition, this thesis characterizes the sensitivity of these impacts to variations in the projection of non-aviation anthropogenic emissions (referred to as background emissions). Finally, the implication of a future-year background emissions scenario on the current policy analysis tool, the response surface model (RSMv2), is discussed. Aviation emissions for 2006 are generated using the Aviation Environmental Design Tool (AEDT), while future-year aviation emissions are developed for 2020 and 2030 using the Federal Aviation Administration (FAA) Terminal Area Forecast (TAF) and the International Civil Aviation Organization (ICAO) Committee on Aviation Environmental Protection (CAEP/8) NOx Stringency scenario #6. Background emissions for the year 2005 and 2025 are generated from the US Environmental Protection Agency (EPA) National Emissions Inventory (NEI), and two additional sensitivity scenarios are derived from the emissions forecasts. Uncertainties in present and forecast aviation and background emissions are also characterized. The Community Multiscale Air Quality (CMAQ) model is evaluated to quantify its performance in predicting ambient PM2.5 and ozone concentrations, and it is used to estimate aviation air quality impacts of aviation. Future-year aviation particulate matter (PM2.5) concentrations are found to increase by a factor of 2 and 2.4 by 2020 and 2030, and are dominated by nitrate and ammonium PM. Aviation 8-hour daily maximum ozone is seen to grow by a factor of 1.9 and 2.2 by 2020 and 2030, with non-homogeneous spatial impacts. Aviation PM2.5 varies by +/-25% with a +/-50% variation of the forecast change in background emissions, while changes in ozone impacts are less symmetric at +34%/-21%. The RSMv2 is shown to under-predict future-year aviation nitrate and ammonium PM2.5. Finally, the implications of these results on the aviation industry and on aviation policy are discussed.by Akshay Ashok.S.M

Left ventricular dysfunction by strain echocardiography in thalassemia patients: a pilot study

Background: To evaluate the myocardial function and its correlation with serum ferritin and the number of transfusions in beta-thalassemia major patients by using standard echocardiography and left ventricular strain imaging.Methods: This was a cross-sectional exploration study comprised of 56 beta-thalassemia patients conducted at a tertiary-care center in India between September 2016 and August 2017. Patients with age less than 18 years, diagnosed with thalassemia major, recipients of >20 units of blood transfusions, and normal Left Ventricular (LV) function by 2D-echocardiography were included in the study. Severity of iron overload was determined by using serum ferritin levels and LV strain imaging parameters were evaluated by using strain values of 17 LV segments.Results: A total of 56 beta-thalassemia patients were included in the study. Of these, 29(51.8%) patients were boys and 27(48.2%) patients were girls with a mean age of 7.8±1.84 years. Average serum ferritin level was found to be 4089.83 ng/dl. Strain values of the basal lateral wall of the left ventricle were significantly abnormal in patients who received more (>80) transfusions compared with those who received lesser transfusions (p=0.025 and p=0.045), respectively. Patients with serum ferritin >6000 ng/ml had impaired strain (p=0.03).Conclusions: Conventional echocardiographic parameters and Left Ventricular Ejection Fraction (LVEF) do not provide adequate information about LV dysfunction. Systolic strain index imaging of the LV indicated the presence of early LV systolic dysfunction in patients who received a greater number of blood transfusions and patients with higher serum ferritin levels

Geospatial Epidemiology of chicken-pox disease in India between 2015-2021: A GIS based analysis

Introduction: In this paper, we introduce geographical information systems (GIS) as a tool to study trends in disease spread in time and space. Based on data gathered by the integrated disease surveillance programme (IDSP), we can see where outbreaks of Chickenpox have occurred. Objective: The aim of this study is to assess the trends in chickenpox diseases in India between January 2015 and April 2021 using GIS maps. Methods: For the collection of secondary data relating to chickenpox, a free app called collect 5 was used for collecting data weekly from the IDSP website and then storing them in an online server. In this project, variables that needed to be processed with QGIS were combined with table attributes of many shapefiles of India and presented as maps. Results: Between Jan 2015 and May 2021, 1269 chickenpox outbreaks (27,257 cases) have been recorded. Thirty-one deaths have been confirmed, with most occurring in Bihar and Uttar Pradesh. Nineteen states did not report any deaths. According to the seasonally adjusted trend, the number of cases was highest during the months of January and March. Conclusion:  In summary, geographic information systems have become an invaluable tool for mapping the hotspots of acute epidemics and planning public health interventions to prevent the spread of these diseases

PMUT-Powered Photoacoustic Detection: Revolutionizing Microfluidic Concentration Measurements

This report introduces a novel optofluidic platform based on piezo-MEMS technology, capable of identifying subtle variations in the fluid concentration. The system utilizes piezoelectric micromachined ultrasound transducers (PMUTs) as receivers to capture sound waves produced by nanosecond photoacoustic (PA) pulses emanating from a fluid target housed in PDMS microchannels. Additionally, a dedicated low-noise single-channel amplifier has been developed to extract the minute analog voltage signals from the PMUTs, given the inherently weak ultrasound signals generated by fluid targets. The PMUTs' proficiency in detecting changes in fluid concentration under both static and time-varying conditions has been documented and verified, confirming the platform's efficacy in monitoring fluid concentrations.Comment: 9 pages, 10 figure

Impact of the Volkswagen emissions control defeat device on US public health

The US Environmental Protection Agency (EPA) has alleged that Volkswagen Group of America (VW) violated the Clean Air Act (CAA) by developing and installing emissions control system 'defeat devices' (software) in model year 2009–2015 vehicles with 2.0 litre diesel engines. VW has admitted the inclusion of defeat devices. On-road emissions testing suggests that in-use NO[subscript x] emissions for these vehicles are a factor of 10 to 40 above the EPA standard. In this paper we quantify the human health impacts and associated costs of the excess emissions. We propagate uncertainties throughout the analysis. A distribution function for excess emissions is estimated based on available in-use NO[subscript x] emissions measurements. We then use vehicle sales data and the STEP vehicle fleet model to estimate vehicle distance traveled per year for the fleet. The excess NO[subscript x] emissions are allocated on a 50 km grid using an EPA estimate of the light duty diesel vehicle NO[subscript x] emissions distribution. We apply a GEOS-Chem adjoint-based rapid air pollution exposure model to produce estimates of particulate matter and ozone exposure due to the spatially resolved excess NO[subscript x] emissions. A set of concentration-response functions is applied to estimate mortality and morbidity outcomes. Integrated over the sales period (2008–2015) we estimate that the excess emissions will cause 59 (95% CI: 10 to 150) early deaths in the US. When monetizing premature mortality using EPA-recommended data, we find a social cost of ~$450m over the sales period. For the current fleet, we estimate that a return to compliance for all affected vehicles by the end of 2016 will avert ~130 early deaths and avoid ~$840m in social costs compared to a counterfactual case without recall

Does race impact functional outcomes in patients undergoing robotic partial nephrectomy?

Background: The role of race on functional outcomes after robotic partial nephrectomy (RPN) is still a matter of debate. We aimed to evaluate the clinical and pathologic characteristics of African American (AA) and Caucasian patients who underwent RPN and analyzed the association between race and functional outcomes. Methods: Data was obtained from a multi-institutional database of patients who underwent RPN in 6 institutions in the USA. We identified 999 patients with complete clinical data. Sixty-three patients (6.3%) were AA, and each patient was matched (1:3) to Caucasian patients by age at surgery, gender, Charlson Comorbidity Index (CCI) and renal score. Bivariate and multivariate logistic regression analyses were used to evaluate predictors of acute kidney injury (AKI). Kaplan-Meier method and multivariable semiparametric Cox regression analyses were performed to assess prevalence and predictors of significant eGFR reduction during follow-up. Results: Overall, 252 patients were included. AA were more likely to have hypertension (58.7% Conclusions: Although African American patients were more likely to have hypertension, renal function outcomes of robotic partial nephrectomies were not significantly different when stratified by race. However, future studies with larger cohorts are necessary to validate these findings

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Reducing the air quality impacts of aircraft activity at airports

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2016.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 140-161).Air transportation is an integral part of the economy and the transportation infrastructure. However, aircraft activity at airports generates CO2 emissions that affect the climate and other pollutants that affect air quality and human health. The focus of this thesis is to enable the reduction of the air quality impacts of aircraft operations at airports by (1) advancing the understanding of the relationship between aircraft activity and its air quality impacts and (2) evaluating the air quality benefits of controlling aircraft operations. There are atmospheric conditions where decreasing fuel burn (which is directly proportional to CO2 emissions) results in increased population exposure to fine particulate matter (PM2.5) and ozone (O3). This thesis quantifies the duration and magnitude of the tradeoffs between CO2 emissions and population exposure. The research complements current studies that optimize aircraft operations at airports for CO2 emissions but have not quantified the air quality implications of doing so. This raises the possibility of reducing the air quality impacts of airports beyond focusing only on minimizing fuel burn. Next, this thesis characterizes the atmospheric conditions that give rise to tradeoffs between emissions and population exposure to ozone. The ozone exposure response to nitrogen oxide (NOx) and Volatile Organic Compound (VOC) emissions is quantified as a function of ambient NOx and VOC concentrations using ozone exposure isopleths. This is the first time that ozone exposure isopleths are created for all locations in the US, using emission sensitivities from the adjoint of an air quality model. Metrics are calculated based on the isopleths which can be used to determine whether NOx and VOC emission reductions will improve ozone exposure or be counter-productive and the optimal NOx/VOC reduction ratio. Finally, this thesis calculates, for the first time, the air quality and climate benefits of pushback control and de-rated takeoffs for simulated aircraft operations at the Detroit Metropolitan Wayne County Airport (DTW). Operations are also optimized for minimum air quality, environmental and fuel combustion-related costs. The results show that the gate holding strategy is effective in mitigating the environmental impacts of taxi operations at airports, reducing CO2 emissions and air quality impacts by 35-40% relative to a baseline without gate holds. De-rated takeoffs at 75% thrust are effective in reducing the air quality impacts of takeoff operations by 19% but increase fuel burn by 3% relative to full-thrust takeoffs. Environmental costs are minimized with average optimal thrust setting of 81%, while maintenance cost savings are estimated to be 2 orders of magnitude larger than the increase in fuel costs from de-rated takeoffs.by Akshay Ashok.Ph. D

Malware Detection in Android platform using DNN

The android platform market is growing exponentially and so the attacks on android platform are increased. The attacks usually performed by installing an android application with malicious code inside the application. On initializing the malicious application an attacker is able to get device access, network information and so on. In the past, many researchers have performed research on this problem. This research is performed aiming to solve and add extra layer of defence in android platform using deep learning technology. The research is carried out by developing hybrid malware detection models in which static model was developed using static features of an android application such as manifest permissions, Intents and API calls whereas the dynamic model was developed using dynamic features such as system calls and system binder calls. The recurrent neural network particularly Long Short-Term Memory technique is utilized to developed both the models. Both the static and dynamic models are trained and the efficiency of the models is analysed using confusion matric and roc & auc scores. The developed models will be used in the organisation to add an extra layer of security in their current working mobile threat detection system