This thesis consists of work conducted in two separate studies, evaluating the performance of passive systems for treating wastewater effluents. The first study involved the characterization of three wastewater stabilization ponds (WSPs) providing secondary and tertiary treatment for municipal wastewater at a facility in Amherstview, Ontario, Canada. Since 2003, the WSPs have experienced excessive algae growth and high pH levels during the summer months. A full range of parameters consisting of: pH, chlorophyll-a (chl-a), dissolved oxygen (DO), temperature, alkalinity, oxidation-reduction potential (ORP), conductivity, nutrient species, and organic matter measures; were monitored for the system and the chemical dynamics in the three WSPs were assessed through multivariate statistical analysis. Supplementary continuous monitoring of pH, chl-a, and DO was performed to identify time-series dependencies. The analyses showed strong correlations between chl-a and sunlight, temperature, organic matter, and nutrients, and strong time dependent correlations between chl-a and DO and between chl-a and pH. Additionally, algae samples were collected and analyzed using metagenomics methods to determine the distribution and speciation of algae growth in the WSPs. A strong shift from the dominance of a major class of green algae, chlorophyceae, in the first WSP, to the dominance of land plants, embryophyta – including aquatic macrophytes – in the third WSP, was observed and corresponded to field observations during the study period. The second study involved the evaluation of the performance and chemical dynamics of a hybrid-passive system treating leachate from a municipal solid waste (MSW) landfill in North Bay, Ontario, Canada. Over a three year period, monitoring of a full range of parameters consisting of: pH, DO, temperature, alkalinity, ORP, conductivity, sulfate, chloride, phenols, solids fractions, nutrient species, organic matter measures, and metals; was conducted bi-weekly and the dataset was analyzed with time series and multivariate statistical techniques. Regression analyses identified 8 parameters that were most frequently retained for modelling the five criteria parameters (alkalinity, ammonia, chemical oxygen demand, iron, and heavy metals), on a statistically significant level (p < 0.05): conductivity, DO, nitrite, organic nitrogen, ORP, pH, sulfate, and total volatile solids. / Thesis (Master, Civil Engineering) -- Queen's University, 2013-10-27 05:29:20.564

A population based cross-sectional time series analysis was conducted using three Ontario administrative databases, to assess trends over time in the surgical management of kidney stone disease. All kidney stone treatments performed with extracorporeal shockwave lithotripsy (SWL), ureteroscopy (URS) and percutaneous nephrolithotomy between July 1, 1991 and December 31, 2010, were included. Time series modeling with exponential smoothing and autoregressive integrated moving average models demonstrated a significant increase in the utilization of URS over time (23.69% to 59.98%, p<0.0001), with a reciprocal significant decrease in the utilization of SWL (68.77% to 33.36%, p<0.0001). As a result of this shift in treatment paradigm, time series modeling also demonstrated an associated significant decrease in the need for ancillary treatment over time (22.12% to 16.01%, p<0.0001) and a significant increase in the need for hospital readmission (8.01% to 10.85%, p<0.0001) or emergency room visit (7.58% to 9.95%, p=0.0024) within 7 days following treatment.

A population based cross-sectional time series analysis was conducted using three Ontario administrative databases, to assess trends over time in the surgical management of kidney stone disease. All kidney stone treatments performed with extracorporeal shockwave lithotripsy (SWL), ureteroscopy (URS) and percutaneous nephrolithotomy between July 1, 1991 and December 31, 2010, were included. Time series modeling with exponential smoothing and autoregressive integrated moving average models demonstrated a significant increase in the utilization of URS over time (23.69% to 59.98%, p<0.0001), with a reciprocal significant decrease in the utilization of SWL (68.77% to 33.36%, p<0.0001). As a result of this shift in treatment paradigm, time series modeling also demonstrated an associated significant decrease in the need for ancillary treatment over time (22.12% to 16.01%, p<0.0001) and a significant increase in the need for hospital readmission (8.01% to 10.85%, p<0.0001) or emergency room visit (7.58% to 9.95%, p=0.0024) within 7 days following treatment.

This thesis is concerned, loosely, with time series analysis. It is also, loosely, concerned with smoothers and Generalized Additive Models. And, finally, it is also concerned with the estimation of health risk due to air pollution. In the field of time series analysis, we develop two data-driven interpolation algorithms for interpolation of mixed time series data; that is, data which has a stationary or “almost” stationary background with embedded deterministic trend and sinusoidal components. These interpolators are developed to deal with the problem of estimating power spectra under the condition that some observations of the series are unavailable. We examine the structure of time-based cubic regression spline smoothers in Generalized Additive Models and demonstrate several interpretation problems with the resultant models. We propose, implement, and test a replacement smoother and show dramatic improvement. We further demonstrate a new, spectrally motivated way of examining residuals in Generalized Additive Models which drives many of the findings of this thesis. Finally, we create and analyze a large-scale Canadian air pollution and mortality database. In the course of analyzing the data we rebuild the standard risk estimation model and demonstrate several improvements. We conclude with a comparison of the original model and the updated model and show that the new model gives consistently more positive risk estimates. / Thesis (Ph.D, Mathematics & Statistics) -- Queen's University, 2012-10-26 14:32:00.678

Statistical analysis of time series. With compelling arguments we show that the Diffusion Entropy Analysis (DEA) is the only method of the literature of the Science of Complexity that correctly determines the scaling hidden within a time series reflecting a Complex Process. The time series is thought of as a source of fluctuations, and the DEA is based on the Shannon entropy of the diffusion process generated by these fluctuations. All traditional methods of scaling analysis, instead, are based on the variance of this diffusion process. The variance methods detect the real scaling only if the Gaussian assumption holds true. We call H the scaling exponent detected by the variance methods and d the real scaling exponent. If the time series is characterized by Fractional Brownian Motion, we have H¹d and the scaling can be safely determined, in this case, by using the variance methods. If, on the contrary, the time series is characterized, for example, by Lévy statistics, H ¹ d and the variance methods cannot be used to detect the true scaling. Lévy walk yields the relation d=1/(3-2H). In the case of Lévy flights, the variance diverges and the exponent H cannot be determined, whereas the scaling d exists and can be established by using the DEA. Therefore, only the joint use of two different scaling analysis methods, the variance scaling analysis and the DEA, can assess the real nature, Gauss or Lévy or something else, of a time series. Moreover, the DEA determines the information content, under the form of Shannon entropy, or of any other convenient entopic indicator, at each time step of the process that, given a sufficiently large number of data, is expected to become diffusion with scaling. This makes it possible to study the regime of transition from dynamics to thermodynamics, non-stationary regimes, and the saturation regime as well. First of all, the efficiency of the DEA is proved with theoretical arguments and with numerical work on artificial sequences. Then we apply the DEA to three different sets of real data, Genome sequences, hard x-ray solar flare waiting times and sequences of sociological interest. In all these cases the DEA makes new properties, overlooked by the standard method of analysis, emerge.

The results of a statistical study of hard x-ray solar flares are presented in this dissertation. Two methods of analysis were used, the Diffusion Entropy (DE) method coupled with an analysis of the data distributions and the Rescaled Range (R/S) Method, sometimes referred to as "Hurst's method". Chapter one provides an introduction to hard x-ray flares within the context of the solar environment and a summary of the statistical paradigms solar astronomers currently work under. Chapter two presents the theory behind the DE and R/S methods. Chapter three presents the results of the two analysis methodologies: most notably important evidence of the conflicting results of the R/S and DE methods, evidence of a Levy statistical signature for the underlying dynamics of the hard x-ray flaring process and a possible separate memory signature for the waiting times. In addition, the stationary and nonstationary characteristics of the waiting times and peak intensities, are revealed. Chapter four provides a concise summary and discussion of the results.

Springs in the Middle Atlas Mountains of Morocco are significant sources for public water supply. From March 2014 to May 2015, water temperatures were measured hourly at three springs (Ribaa, Sidi Rached, and Zerouka); water levels were measured hourly at Sidi Rached and Zerouka; and daily turbidity data were obtained from Ribaa. From March 2014 to March 2015, daily water samples were taken at Zerouka for analyses of the stable isotopes deuterium and oxygen-18. Hourly weather data (precipitation and air temperature) were available from March 2014 to May 2015 from Ifrane, near Zerouka. Temperature responses varied between the springs, showing a time-lagged seasonal signal at Sidi Rached, near-constant values at Zerouka, and relatively stable dry-season values followed by flashy wet-season behavior at Ribaa. Stage at Sidi Rached and Zerouka tracked together, with a broad minimum in late summer and responses to individual storms superposed on the signal. Stable isotopes fluctuated daily but were frequently out of phase with each other. Autocorrelation analyses of spring parameters indicate that Sidi Rached and Zerouka have greater inertia than Ribaa. Cross-correlation analyses show characteristic time lags between (1) precipitation and stage, (2) air temperature and water isotopes, and (3) air and water temperatures. However, as shown in previous work, there is a broad range of time lags between precipitation and turbidity. The variety of spring behaviors is consistent with differences in hydraulic connectivity within each spring basin.

This thesis investigates the association between alcohol consumption and alcohol-related harm in Eastern Europe. The main aim was to estimate to what extent changes in per capita alcohol consumption have an impact on different forms of alcohol-related mortality, and to put the results in an international comparative perspective. The thesis includes four papers; the first two papers use aggregate time-series analysis to assess how changes in per capita consumption affect rates in suicide mortality and fatal non-intentional injuries in several Eastern European countries, respectively. The third paper applies the same methodological approach to analyse the population-level relationship between alcohol and homicide in Russia and the U.S.. The fourth paper employs survey data to assess how the risk of experiencing alcohol-related problems in relation to volume of consumption in the Baltic countries compares to Sweden and Italy. The results of the first three papers suggests: (i) that changes in per capita consumption are significantly related to changes in mortality rates of suicide, non-intentional injuries and homicide in the countries under study; (ii) that the relationship is stronger for men than for women, and (iii) that the relationship tends to be stronger in the countries with more detrimental drinking patterns, e.g. Russia. The results of the fourth paper suggest that the risk of experiencing alcohol-related problems in relation to level of drinking in the Baltic countries is similar to the corresponding risk in Sweden, but considerably stronger than in Italy. In conclusion, the findings support the significance of a public health approach to alcohol-related problems in Eastern Europe, i.e., policy measures directed towards total alcohol consumption. In addition, strategies aimed at reducing the occurrence of binge drinking seem to have great potential for reducing alcohol-related harm and mortality in Eastern European countries. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Manuscript. Paper 4: Manuscript.</p> / Alcohol in Eastern Europe - a Public Health Perspective

In this thesis, a highly comparative framework for time-series analysis is developed. The approach draws on large, interdisciplinary collections of over 9000 time-series analysis methods, or operations, and over 30 000 time series, which we have assembled. Statistical learning methods were used to analyze structure in the set of operations applied to the time series, allowing us to relate different types of scientific methods to one another, and to investigate redundancy across them. An analogous process applied to the data allowed different types of time series to be linked based on their properties, and in particular to connect time series generated by theoretical models with those measured from relevant real-world systems. In the remainder of the thesis, methods for addressing specific problems in time-series analysis are presented that use our diverse collection of operations to represent time series in terms of their measured properties. The broad utility of this highly comparative approach is demonstrated using various case studies, including the discrimination of pathological heart beat series, classification of Parkinsonian phonemes, estimation of the scaling exponent of self-affine time series, prediction of cord pH from fetal heart rates recorded during labor, and the assignment of emotional content to speech recordings. Our methods are also applied to labeled datasets of short time-series patterns studied in temporal data mining, where our feature-based approach exhibits benefits over conventional time-domain classifiers. Lastly, a feature-based dimensionality reduction framework is developed that links dependencies measured between operations to the number of free parameters in a time-series model that could be used to generate a time-series dataset.

Background: Dabbing is an emerging method of marijuana ingestion. However, little is known about dabbing owing to limited surveillance data on dabbing. Objective: The aim of the study was to analyze Google search data to assess the scope and breadth of information seeking on dabbing. Methods: Google Trends data about dabbing and related topics (eg, electronic nicotine delivery system [ENDS], also known as e-cigarettes) in the United States between January 2004 and December 2015 were collected by using relevant search terms such as "dab rig." The correlation between dabbing (including topics: dab and hash oil) and ENDS (including topics: vaping and e-cigarette) searches, the regional distribution of dabbing searches, and the impact of cannabis legalization policies on geographical location in 2015 were analyzed. Results: Searches regarding dabbing increased in the United States over time, with 1,526,280 estimated searches during 2015. Searches for dab and vaping have very similar temporal patterns, where the Pearson correlation coefficient (PCC) is .992 (P<.001). Similar phenomena were also obtained in searches for hash oil and e-cigarette, in which the corresponding PCC is .931 (P<.001). Dabbing information was searched more in some western states than other regions. The average dabbing searches were significantly higher in the states with medical and recreational marijuana legalization than in the states with only medical marijuana legalization (P=.02) or the states without medical and recreational marijuana legalization (P=.01). Conclusions: Public interest in dabbing is increasing in the United States. There are close associations between dabbing and ENDS searches. The findings suggest greater popularity of dabs in the states that legalized medical and recreational marijuana use. This study proposes a novel and timely way of cannabis surveillance, and these findings can help enhance the understanding of the popularity of dabbing and provide insights for future research and informed policy making on dabbing.