Comparing universal kriging and land-use regression for predicting concentrations of gaseous oxides of nitrogen (NOx) for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air)

BACKGROUND: Epidemiological studies that assess the health effects of long-term exposure to ambient air pollution are used to inform public policy. These studies rely on exposure models that use data collected from pollution monitoring sites to predict exposures at subject locations. Land use regression (LUR) and universal kriging (UK) have been suggested as potential prediction methods. We evaluate these approaches on a dataset including measurements from three seasons in Los Angeles, CA. METHODS: The measurements of gaseous oxides of nitrogen (NOx) used in this study are from a "snapshot" sampling campaign that is part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). The measurements in Los Angeles were collected during three two-week periods in the summer, autumn, and winter, each with about 150 sites. The design included clusters of monitors on either side of busy roads to capture near-field gradients of traffic-related pollution. LUR and UK prediction models were created using geographic information system (GIS)-based covariates. Selection of covariates was based on 10-fold cross-validated (CV) R(2) and root mean square error (RMSE). Since UK requires specialized software, a computationally simpler two-step procedure was also employed to approximate fitting the UK model using readily available regression and GIS software. RESULTS: UK models consistently performed as well as or better than the analogous LUR models. The best CV R(2) values for season-specific UK models predicting log(NOx) were 0.75, 0.72, and 0.74 (CV RMSE 0.20, 0.17, and 0.15) for summer, autumn, and winter, respectively. The best CV R(2) values for season-specific LUR models predicting log(NOx) were 0.74, 0.60, and 0.67 (CV RMSE 0.20, 0.20, and 0.17). The two-stage approximation to UK also performed better than LUR and nearly as well as the full UK model with CV R(2) values 0.75, 0.70, and 0.70 (CV RMSE 0.20, 0.17, and 0.17) for summer, autumn, and winter, respectively. CONCLUSION: High quality LUR and UK prediction models for NOx in Los Angeles were developed for the three seasons based on data collected for MESA Air. In our study, UK consistently outperformed LUR. Similarly, the 2-step approach was more effective than the LUR models, with performance equal to or slightly worse than UK.     

Free market ideology and deregulation in Colorado’s oil fields: Evidence for triple movement activism?

Unconventional oil and gas extraction (UOGE) has spurred an unprecedented boom in onshore production in the US. Despite a surge in related research, a void exists regarding inquiries into policy outcomes and perceptions. To address this, support for federal regulatory exemptions for UOGE is examined using survey data collected in 2015 from two Northern Colorado communities. Current regulatory exemptions for UOGE can be understood as components of broader societal processes of neoliberalization. Free market ideology increases public support for federal regulatory exemptions for UOGE. Perceived negative impacts do not necessarily drive people to support increased federal regulation. Utilizing neo-Polanyian theory, interaction between free market ideology and perceived negative impacts is explored. Free market ideology appears to moderate people’s views of regulation: increasing the effect of perceived negative impacts while simultaneously increasing support for deregulation. To conclude, the ways in which free market ideology might normalize the impacts of UOGE activity are discussed.     

Simulated climate adaptation in stormwater systems: evaluating the efficiency of adaptation strategies

Adaptations in infrastructure may be necessitated by changes in temperature and precipitation patterns to avoid losses and maintain expected levels of service. A roster of adaptation strategies has emerged in the climate change literature, especially with regard to timing: anticipatory, concurrent, or reactive. Significant progress has been made in studying climate change adaptation decision making that incorporates uncertainty, but less work has examined how strategies interact with existing infrastructure characteristics to influence adaptability. We use a virtual testbed of highway drainage crossings configured with a selection of actual culvert emplacements in Colorado, USA, to examine the effect of adaptation strategy and culvert characteristics on cost efficiency and service level under varying rates of climate change. A meta-model approach with multinomial regression is used to compare the value of better climate change predictions with better knowledge of existing crossing characteristics. We find that, for a distributed system of infrastructural units like culverts, knowing more about existing characteristics can improve the efficacy of adaptation strategies more than better projections of climate change. Transportation departments choosing climate adaptation strategies often lack detailed data on culverts, and gathering that data could improve the efficiency of adaptation despite climate uncertainty.     

Why farmers adopt best management practice in the United States: a meta-analysis of the adoption literature

This meta-analysis of both published and unpublished studies assesses factors believed to influence adoption of agricultural Best Management Practices in the United States. Using an established statistical technique to summarize the adoption literature in the United States, we identified the following variables as having the largest impact on adoption: access to and quality of information, financial capacity, and being connected to agency or local networks of farmers or watershed groups. This study shows that various approaches to data collection affect the results and comparability of adoption studies. In particular, environmental awareness and farmer attitudes have been inconsistently used and measured across the literature. This meta-analysis concludes with suggestions regarding the future direction of adoption studies, along with guidelines for how data should be presented to enhance the adoption of conservation practices and guide research.     

A survey on the utility of the USEPA CADDIS stressor identification procedure

The Environmental Protection Agency (EPA) has made available on the worldwide web a systematic stream stressor identification procedure, the “Causal Analysis/Diagnosis Decision Information System” or CADDIS. We report here the results of a survey of regulators and scientists in 11 states who use CADDIS or another stressor identification procedure in their work. The 13 survey questions address guidelines as to what impairment scenarios to approach with stressor identification, what information is needed to perform stressor identification, and what the stakeholder role is in performing stressor identification. At the time of this survey (the summer of 2009), the EPA CADDIS website was less commonly used among the state regulators surveyed than the published EPA stressor identification document on which it is based. The respondents generally find the EPA stressor identification procedure useful and capable of being adapted to their individual needs. Survey respondents all use stressor identification in their Total Maximum Daily Load work, but also in a wide variety of other applications. All the “types of evidence” included in the CADDIS stressor identification procedure are used by the practitioners surveyed with the exception of the results of ecological simulation models. While the CADDIS documentation encourages the involvement of stakeholders in stressor identification, most respondents do not assemble stakeholder teams of local officials and citizens to participate in stressor analyses.     

The use of multiple tracers for tracking wastewater discharges in freshwater systems

The assessment of potential impacts of wastewater effluent discharges in freshwater systems requires an understanding of the likely degrees of dilution and potential zones of influence. In this study, four tracers commonly present in wastewater effluents were monitored to compare their relative effectiveness in determining areas in freshwater systems that are likely to be impacted by effluent discharges. The four tracers selected were the human pharmaceutical carbamazepine, anthropogenic gadolinium, fluorescent-dissolved organic matter (fDOM), and electrical conductivity (EC). The four tracers were monitored longitudinally in two distinct freshwater systems receiving wastewater effluents, where one site had a high level of effluent dilution (effluent <1 % of total flow) and the other site had a low level of effluent dilution (effluent ～50 % of total flow). At both sites, the selected tracers exhibited a similar pattern of response intensity downstream of discharge points relative to undiluted wastewater effluent, although a number of anomalies were noted between the tracers. Both EC and fDOM are non-specific to human influences, and both had a high background response, relative to the highly sensitive carbamazepine and anthropogenic gadolinium responses, although the ease of measuring EC and fDOM would make them more adaptable in highly variable systems. However, the greater sensitivity and selectivity of carbamazepine and gadolinium would make their combination with EC and fDOM as tracers of wastewater effluent discharges highly desirable to overcome potential limitations of individual tracers.     

Design and evaluation of an inlet conditioner to dry particles for real-time particle sizers

Real-time particle sizers provide rapid information about atmospheric particles, particularly peak exposures, which may be important in the development of adverse health outcomes. However, these instruments are subject to erroneous readings in high-humidity environments when compared with measurements from filter-based, federal reference method (FRM) samplers. Laboratory tests were conducted to evaluate the ability of three inlet conditioners to dry aerosol prior to entering a real-time particle sizer for measuring coarse aerosols (Model 3321 Aerodynamic Particle Sizer, APS) under simulated highly humid conditions. Two 30 day field studies in Birmingham, AL, USA were conducted to compare the response of two APSs operated with and without an inlet conditioner to that measured with FRM samplers. In field studies, the correlation of PM(10-2.5) derived from the APS and that measured with the FRM was substantially stronger with an inlet conditioner applied (r2 ranged from 0.91 to 0.99) than with no conditioner (r2 = 0.61). Laboratory experiments confirmed the ability of the heater and desiccant conditioner to remove particle-borne moisture. In field tests, water was found associated with particles across the sizing range of the APS (0.5 microm to 20 microm) when relative humidity was high in Birmingham. Certain types of inlet conditioners may substantially improve the correlation between particulate mass concentration derived from real-time particle sizers and filter-based samplers in humid conditions.     

Recent biosensing developments in environmental security

Environmental security is one of the fundamental requirements of our well being. However, it still remains a major global challenge. Therefore, in addition to reducing and/or eliminating the amounts of toxic discharges into the environment, there is need to develop techniques that can detect and monitor these environmental pollutants in a sensitive and selective manner to enable effective remediation. Because of their integrated nature, biosensors are ideal for environmental monitoring and detection as they can be portable and provide selective and sensitive rapid responses in real time. In this review we discuss the main concepts behind the development of biosensors that have most relevant applications in the field of environmental monitoring and detection. We also review and document recent trends and challenges in biosensor research and development particularly in the detection of species of environmental significance such as organophosphate nerve agents, heavy metals, organic contaminants, pathogenic microorganisms and their toxins. Special focus will be given to the trends that have the most promising applications in environmental security. We conclude by highlighting the directions towards which future biosensors research in environmental security sector might proceed.