Publications

Pesticides have many different properties that affect their behaviour in the environment. Pesticide’s solubility
in water has a great impact on leaching potential and environmental fate. The objective of this study was to determine the
relationship between organic carbon based partition coefficient (koc) and pesticides solubility (S) of pesticides used along the
shore of Lake Naivasha using regression analysis. The properties (S, and soil/water equilibrium partition coefficient (kd)) of
pesticides selected from an inventory of pesticides used in farms around Lake Naivasha, were determined from the
manufacturers’ materials safety data sheets. The organic carbon (foc) of the soil from the study area was then determined using
the loss-on-ignition (LOI) method and used to calculate koc. The results showed that the soils around Lake Naivasha had a
mean organic carbon (foc) content of 1.770% and a regression equation for koc and S for the area to be log koc = -0.368logS +
3.256. It was concluded that this relationship can be used to estimate the organic carbon based partition coefficient (koc) of a
pesticide where S is available, and the results compared with values determined experimentally and from other models.

In this work, ZnO thin films were investigated to sense NO2, a gas exhausted by the most common combustion systems polluting the environment. To this end, ZnO thin films were grown by RF sputtering on properly designed and patterned substrates to allow the measurement of the electrical response of the material when exposed to different concentrations of the gas. X-ray diffraction was carried out to correlate the material’s electrical response to the morphological and microstructural features of the sensing materials. Electrical conductivity measurements showed that the transducer fabricated in this work exhibits the optimal performance when heated at 200 °C, and the detection of 0.1 ppm concentration of NO2 was possible. Ab initio modeling allowed the understanding of the sensing mechanism driven by the competitive adsorption of NO2 and atmospheric oxygen mediated by heat. The combined theoretical and experimental study here reported provides insights into the sensing mechanism which will aid the optimization of ZnO transducer design for the quantitative measurement of NO2 exhausted by combustion systems which will be used, ultimately, for the optimized adjustment of combustion resulting into a reduced pollutants and greenhouse gases emission.

© 2016 The Author(s).Background: Motor vehicle collisions (MVCs) are a substantial contributor to the global burden of disease and lead to subsequent post-traumatic stress disorder (PTSD). However, the relevant literature originates in only a few countries, and much remains unknown about MVC-related PTSD prevalence and predictors. Methods: Data come from the World Mental Health Survey Initiative, a coordinated series of community epidemiological surveys of mental disorders throughout the world. The subset of 13 surveys (5 in high income countries, 8 in middle or low income countries) with respondents reporting PTSD after life-threatening MVCs are considered here. Six classes of predictors were assessed: socio-demographics, characteristics of the MVC, childhood family adversities, MVCs, other traumatic experiences, and respondent history of prior mental disorders. Logistic regression was used to examine predictors of PTSD. Mental disorders were assessed with the fully-structured Composite International Diagnostic Interview using DSM-IV criteria. Results: Prevalence of PTSD associated with MVCs perceived to be life-threatening was 2.5 {%} overall and did not vary significantly across countries. PTSD was significantly associated with low respondent education, someone dying in the MVC, the respondent or someone else being seriously injured, childhood family adversities, prior MVCs (but not other traumatic experiences), and number of prior anxiety disorders. The final model was significantly predictive of PTSD, with 32 {%} of all PTSD occurring among the 5 {%} of respondents classified by the model as having highest PTSD risk. Conclusion: Although PTSD is a relatively rare outcome of life-threatening MVCs, a substantial minority of PTSD cases occur among the relatively small proportion of people with highest predicted risk. This raises the question whether MVC-related PTSD could be reduced with preventive interventions targeted to high-risk survivors using models based on predictors assessed in the immediate aftermath of the MVCs.

Copyright © Cambridge University Press 2016Background: Research on post-traumatic stress disorder (PTSD) following natural and human-made disasters has been undertaken for more than three decades. Although PTSD prevalence estimates vary widely, most are in the 20–40{%} range in disaster-focused studies but considerably lower (3–5{%}) in the few general population epidemiological surveys that evaluated disaster-related PTSD as part of a broader clinical assessment. The World Mental Health (WMH) Surveys provide an opportunity to examine disaster-related PTSD in representative general population surveys across a much wider range of sites than in previous studies. Method: Although disaster-related PTSD was evaluated in 18 WMH surveys, only six in high-income countries had enough respondents for a risk factor analysis. Predictors considered were socio-demographics, disaster characteristics, and pre-disaster vulnerability factors (childhood family adversities, prior traumatic experiences, and prior mental disorders). Results: Disaster-related PTSD prevalence was 0.0–3.8{%} among adult (ages 18+) WMH respondents and was significantly related to high education, serious injury or death of someone close, forced displacement from home, and pre-existing vulnerabilities (prior childhood family adversities, other traumas, and mental disorders). Of PTSD cases 44.5{%} were among the 5{%} of respondents classified by the model as having highest PTSD risk. Conclusion: Disaster-related PTSD is uncommon in high-income WMH countries. Risk factors are consistent with prior research: severity of exposure, history of prior stress exposure, and pre-existing mental disorders. The high concentration of PTSD among respondents with high predicted risk in our model supports the focus of screening assessments that identify disaster survivors most in need of preventive interventions.

Background: Unexpected death of a loved one (UD) is the most commonly reported traumatic experience in cross-national surveys. However, much remains to be learned about posttraumatic stress disorder (PTSD) after this experience. The WHO World Mental Health (WMH) survey ini-tiative provides a unique opportunity to address these issues. Methods: Data from 19 WMH surveys (n = 78,023; 70.1{%} weighted response rate) were collated. Potential predictors of PTSD (respondent sociodemographics, characteristics of the death, history of prior trauma exposure, history of prior mental disorders) after a representative sample of UDs were examined using logistic regression. Simulation was used to estimate overall model strength in targeting individuals at highest PTSD risk. Results: PTSD prevalence after UD averaged 5.2{%} across surveys and did not differ signifi-cantly between high-income and low-middle income countries. Significant multivariate predictors included the deceased being a spouse or child, the respondent being female and believing they could have done something to prevent the death, prior trauma exposure, and history of prior men-tal disorders. The final model was strongly predictive of PTSD, with the 5{%} of respondents having highest estimated risk including 30.6{%} of all cases of PTSD. Positive predictive value (i.e., the pro-portion of high-risk individuals who actually developed PTSD) among the 5{%} of respondents with highest predicted risk was 25.3{%}. Conclusions: The high prevalence and meaningful risk of PTSD make UD a major public health issue. This study provides novel insights into predictors of PTSD after this experience and sug-gests that screening assessments might be useful in identifying high-risk individuals for preventive interventions.