RANK-BASED TEMPO-SPATIAL CLUSTERING: A FRAMEWORK FOR RAPID OUTBREAK DETECTION USING SINGLE OR MULTIPLE DATA STREAMS

In the recent decades, algorithms for disease outbreak detection have become one of the main interests of public health practitioners to identify and localize an outbreak as early as possible in order to warrant further public health response before a pandemic develops. Today’s increased threat of biological warfare and terrorism provide an even stronger impetus to develop methods for outbreak detection based on symptoms as well as definitive laboratory diagnoses. In this dissertation work, I explore the problems of rapid disease outbreak detection using both spatial and temporal information. I develop a framework of non-parameterized algorithms which search for patterns of disease outbreak in spatial sub-regions of the monitored region within a certain period. Compared to the current existing spatial or tempo-spatial algorithm, the algorithms in this framework provide a methodology for fast searching of either univariate data set or multivariate data set. It first measures which study area is more likely to have an outbreak occurring given the baseline data and currently observed data. Then it applies a greedy searching mechanism to look for clusters with high posterior probabilities given the risk measurement for each unit area as heuristic. I also explore the performance of the proposed algorithms. From the perspective of predictive modeling, I adopt a Gamma-Poisson (GP) model to compute the probability of having an outbreak in each cluster when analyzing univariate data. I build a multinomial generalized Dirichlet (MGD) model to identify outbreak clusters from multivariate data which include the OTC data streams collected by the national retail data monitor (NRDM) and the ED data streams collected by the RODS system. Key contributions of this dissertation include 1) it introduces a rank-based tempo-spatial clustering algorithm, RSC, by utilizing greedy searching and Bayesian GP model for disease outbreak detection with comparable detection timeliness, cluster positive prediction value (PPV) and improved running time; 2) it proposes a multivariate extension of RSC (MRSC) which applies MGD model. The evaluation demonstrated the advantage that MGD model can effectively suppress the false alarms caused by elevated signals that are non-disease relevant and occur in all the monitored data streams

Identifying the cardiovascular effects of multiple pollutants.

Cardiovascular disease (CVD) is the leading cause of death from environmental exposures. Although exposure to PM2.5 is an established risk factor for CVD, the contribution of other hazardous pollutant exposure to CVD is less clear. Overall, this work aimed to examine the effect of pollutants with lesser documented effects on cardiovascular disease using a multi-pronged approach to exposure assessment. The three aims were to examine the relationship between county-level toxic chemical releases and CVD mortality in the contiguous United States between 2002 and 2012, to assess the relationship between individual-level VOC metabolites and vascular function, and to build multipollutant models from the previous two aims to assess the role of mixtures and mixture components in CVD mortality and vascular function. In our national, county-level study, we found that for every 25% increase in annual county-level toxic release, we found a 2.8% (1.2, 4.4; p-value=0.0006) increase in CVD mortality rate. We also found that for every 25% increase in annual county-level risk score, there was a 3.0% (95%CI 1.3, 4.6; p-value=0.0003) increase in CVD mortality. Using the multipollutant method, elastic net, we identified five out of 467 potentially toxic chemicals at the county-level: bromoform, dichlorobromomethane, dichlorotrifluoroethane, nitrophenol, and thallium. In our study of individual-level VOC metabolites, we found that the acrolein metabolite, 3HPMA, was positively associated with systolic BP (+0.98 per SD of 3HPMA; CI: 0.04, 1.91; P=0.04). For each IQR of 3HPMA or DHBMA (a 1,3-butadiene metabolite), there was a 3.3% (CI: -6.18, -0.37; p-value: 0.03) or a 4.0% (CI: -7.72, -0.12; P=0.04) decrease in endothelial function. Urinary levels of the 1,3-butadiene metabolite, MHBMA3, were positively associated with a 2.9% increase in urinary epinephrine (CI: 0.48, 5.37; P=0.02). Using the multipollutant method, Bayesian Kernel Machine Regression, we found that the whole mixture of VOC metabolites (CEMA, 3HPMA, DHBMA, MHBMA3, and HPMMA) was significantly associated with blood pressure, which was primarily driven by 3HPMA. Taken together, these findings suggest that exposure to under regulated pollutants like TRI chemicals and VOCs contribute to CVD mortality and vascular dysfunction. Further research is required to corroborate these findings

Integrated human exposure to air pollution

The book “Integrated human exposure to air pollution” aimed to increase knowledge about human exposure in different micro-environments, or when citizens are performing specific tasks, to demonstrate methodologies for the understanding of pollution sources and their impact on indoor and ambient air quality, and, ultimately, to identify the most effective mitigation measures to decrease human exposure and protect public health. Taking advantage of the latest available tools, such as internet of things (IoT), low-cost sensors and a wide access to online platforms and apps by the citizens, new methodologies and approaches can be implemented to understand which factors can influence human exposure to air pollution. This knowledge, when made available to the citizens, along with the awareness of the impact of air pollution on human life and earth systems, can empower them to act, individually or collectively, to promote behavioral changes aiming to reduce pollutants’ emissions. Overall, this book gathers fourteen innovative studies that provide new insights regarding these important topics within the scope of human exposure to air pollution. A total of five main areas were discussed and explored within this book and, hopefully, can contribute to the advance of knowledge in this field

The impact of air pollution on cognitive function

The impact of air pollution (AP) on cognitive function is widely understudied. Currently, most evidence focuses on correlating chronic AP exposure to performance on off-the-shelf cognitive tests in children and ageing participants. This thesis aimed to investigate the impact of both acute and chronic AP exposure on attention, socio-emotional processing, and episodic memory in clinically health adult populations using specially designed behavioural tasks. Using both experimental and quasi-experimental methods, participants were exposed to AP from a range of sources including Particulate Matter (PM) through candle burning; Traffic-related Air Pollution (TRAP) through quasi-experimental measurements during commuting; and Diesel Exhaust (DE) though the use of an atmosphere chamber. Participants in all studies were clinically healthy adults aged between 18 and 50 with no history of cardiovascular or neurological disease. Results indicated a reduction in pro-social behaviour 24 hours following acute exposure to PM and TRAP and lower cognitive control 4 hours following acute exposure to DE. Critically, no immediate effect of acute AP exposure was identified on these functions. The delay between exposure and cognitive dysfunction is suggestive of inflammatory mechanisms as a likely explanation for the identified effects. An immediate deficit in spatiotemporal encoding ability following acute TRAP exposure was identified, suggestive of hypoxia as a mechanistic explanation. However, no episodic encoding difficulties were identified after PM or DE exposure, nor any effect of any AP species on recall ability. This suggests that episodic memory is preserved despite the identified socio-emotional and executive deficits. Chronic exposure was quantified using participant residential postcodes throughout the lifetime. Higher chronic AP exposure was associated with lower cognitive control, indicative of neurodegeneration or stunted neurodevelopment. Together, the findings highlight an impact of AP on the quality and ease of decision making, emotional control, and learning of new information. These processes are critical to successfully navigate the complex ever-changing human environment, and degradation of these processes could lead to risk-taking, aggression, and degradation of mental health

Proceedings of Abstracts 12th International Conference on Air Quality Science and Application

© 2020 The Author(s). This an open access work distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.Final Published versio

Innovative Applications of Laser Remote Sensing of Gases, Aerosols and Wind

Over the years, a major component of the research carried out at the Optical Remote Sensing Laboratory of the City College of New York has been on active sensing technologies and their different applications in atmospheric studies. This thesis builds upon and looks to further advance this field by demonstrating innovative applications of laser remote sensing technologies for studies involving trace gases, aerosol particles and wind; which are key components of the Earth’s atmosphere. First, we present the demonstration of gas concentration measurements using a quantum cascade laser open path system with characteristics that make it promising for mobile and/or multidirectional remote detection of gas leaks. This work looks to address an important environmental concern as fugitive methane emissions from industrial plants and pipelines can contribute to the global increase of greenhouse gas concentration and are a security and safety issue because of the risk of fire, explosion or toxicity. Second, we present horizontal measurements of the spatial distribution of aerosols over New York City using a scanning eye-safe elastic micro-pulse lidar system. Two case studies are presented in which different methodologies are applied in order to estimate the backscatter and extinction coefficients. These observations demonstrate capabilities to monitor local emission sources and rapid transport of aerosols, which are of great importance for air quality monitoring in urban areas due to the harmful effects of particulate pollution on human health. Lastly, we present the analysis of airborne wind measurements using a micro pulse Doppler lidar and comparison against ground measurements. Moreover, in order to evaluate the performance of the airborne system, we investigate some of the factors that may influence wind measurement uncertainty and provide insights on how to improve measurement precision while minimizing errors

Current Air Quality Issues

Air pollution is thus far one of the key environmental issues in urban areas. Comprehensive air quality plans are required to manage air pollution for a particular area. Consequently, air should be continuously sampled, monitored, and modeled to examine different action plans. Reviews and research papers describe air pollution in five main contexts: Monitoring, Modeling, Risk Assessment, Health, and Indoor Air Pollution. The book is recommended to experts interested in health and air pollution issues