Workforce/Population, Economy, Infrastructure, Geography, Hierarchy, and Time (WEIGHT): Reflections on the Plural Dimensions of Disaster Resilience.

The concept of resilience and its relevance to disaster risk management has increasingly gained attention in recent years. It is common for risk and resilience studies to model system recovery by analyzing a single or aggregated measure of performance, such as economic output or system functionality. However, the history of past disasters and recent risk literature suggest that a single-dimension view of relevant systems is not only insufficient, but can compromise the ability to manage risk for these systems. In this article, we explore how multiple dimensions influence the ability for complex systems to function and effectively recover after a disaster. In particular, we compile evidence from the many competing resilience perspectives to identify the most critical resilience dimensions across several academic disciplines, applications, and disaster events. The findings demonstrate the need for a conceptual framework that decomposes resilience into six primary dimensions: workforce/population, economy, infrastructure, geography, hierarchy, and time (WEIGHT). These dimensions are not typically addressed holistically in the literature; often they are either modeled independently or in piecemeal combinations. The current research is the first to provide a comprehensive discussion of each resilience dimension and discuss how these dimensions can be integrated into a cohesive framework, suggesting that no single dimension is sufficient for a holistic analysis of a disaster risk management. Through this article, we also aim to spark discussions among researchers and policymakers to develop a multicriteria decision framework for evaluating the efficacy of resilience strategies. Furthermore, the WEIGHT dimensions may also be used to motivate the generation of new approaches for data analytics of resilience-related knowledge bases.

State of the art in risk analysis of workforce criticality influencing disaster preparedness for interdependent systems.

The objective of this article is to discuss a needed paradigm shift in disaster risk analysis to emphasize the role of the workforce in managing the recovery of interdependent infrastructure and economic systems. Much of the work that has been done on disaster risk analysis has focused primarily on preparedness and recovery strategies for disrupted infrastructure systems. The reliability of systems such as transportation, electric power, and telecommunications is crucial in sustaining business processes, supply chains, and regional livelihoods, as well as ensuring the availability of vital services in the aftermath of disasters. There has been a growing momentum in recognizing workforce criticality in the aftermath of disasters; nevertheless, significant gaps still remain in modeling, assessing, and managing workforce disruptions and their associated ripple effects to other interdependent systems. The workforce plays a pivotal role in ensuring that a disrupted region continues to function and subsequently recover from the adverse effects of disasters. With this in mind, this article presents a review of recent studies that have underscored the criticality of workforce sectors in formulating synergistic preparedness and recovery policies for interdependent infrastructure and regional economic systems.

Stability and Degradation of Opioids in River Water.

As the level of consumption of opioids continues to rise globally, there is increasing concern over the potential impacts of continuous opioid discharges into aquatic ecosystems. Opioids are psychoactive compounds that are not completely removed during wastewater treatment, and little is known about their stability and fate in the environment. In the present study, we evaluated the stability of four highly used opioids, buprenorphine, codeine, fentanyl, and tramadol, in river water via batch degradation experiments. The opioids were spiked at environmentally relevant concentrations into 150 mL of river microcosms designed to distinguish among hydrolysis, abiotic degradation, biodegradation, and sorption. All opioids exhibited relatively high stability in river water, with removal rates of only 15% (tramadol) to 26% (buprenorphine) after 6 days. Biodegradation was the most important attenuation pathway for all four opioids, with first-order biodegradation constants ranging from 0.011 d-1 (tramadol) to 0.018 d-1(buprenorphine). Overall, degradation rates were 1-4 orders of magnitude lower compared to the reported rates for wastewater systems. These results offer insights into the stability of opioids in freshwater systems and raise questions about the potential effects of their pseudopresence in surface waters on aquatic organisms.

Significant regional disparities in riverine microplastics.

Research on riverine microplastics has gradually increased, highlighting an area for further exploration: the lack of extensive, large-scale regional variations analysis due to methodological and spatiotemporal limitations. Herein, we constructed and applied a comprehensive framework for synthesizing and analyzing literature data on riverine microplastics to enable comparative research on the regional variations on a large scale. Research results showed that in 76 rivers primarily located in Asia, Europe, and North America, the microplastic abundance of surface water in Asian rivers was three times higher than that in Euro-America rivers, while sediment in Euro-American rivers was five times more microplastics than Asia rivers, indicating significant regional variations (p < 0.001). Additionally, based on the income levels of countries, rivers in lower-middle and upper-middle income countries had significantly (p < 0.001) higher abundance of microplastics in surface water compared to high-income countries, while the opposite was true for sediment. This phenomenon was preliminarily attributed to varying levels of urbanization across countries. Our proposed framework for synthesizing and analyzing microplastic literature data provides a holistic understanding of microplastic disparities in the environment, and can facilitate broader discussions on management and mitigation strategies.

Development of a Spatial and Temporal Agent-Based Model for Studying Water and Health Relationships: The Case Study of Two Villages in Limpopo, South Africa.

Diarrhea, the second leading cause of child morbidity and mortality, can have detrimental effects in the physical and cognitive development of children in developing countries. Health interventions (e.g., increased access to health services and safe water) designed to address this problem are difficult to implement in resource-limited settings. In this paper, we present a tool for understanding the complex relationship between water and public health in rural areas of a developing country. A spatial and temporal agent-based model (ABM) was developed to simulate the current water, sanitation, and health status in two villages in Limpopo Province, South Africa. The model was calibrated using empirical data and published sources. It was used to simulate the effects of poor water quality on the frequency of diarrheal episodes in children, and consequently on child development. Preliminary simulation results show that at the current total coliform levels in the water sources of the studied villages, children are expected to experience stunting by as much as -1.0 standard deviations from the World Health Organization height norms. With minor modifications, the calibrated ABM can be used to design and evaluate intervention strategies for improving child health in these villages. The model can also be applied to other regions worldwide that face the same environmental challenges and conditions as the studied villages.

Long-term wastewater-based surveillance and impacts of the COVID-19 pandemic on drug use trends in a U.S. Northeast rural town.

Herein we discuss the findings of a two-year wastewater-based drug use surveillance from September 2018 to August 2020 and present objective evidence on the impacts of the COVID-19 pandemic on drug use in a rural community. 24-h composite wastewater samples were collected twice each month from a university town in Northeastern United States and were analyzed for ten priority opioids and stimulants: morphine, codeine, hydrocodone, methadone, fentanyl cocaine, methamphetamine, amphetamine, 3,4-methylenedioxymethamphetamine (MDMA), and 3,4-methylenedioxy-N-ethylamphetamine (MDEA). All target drugs were detected at 100 % frequency in wastewater samples. On a mass basis, the average estimated per capita drug consumption were highest for cocaine, morphine, and amphetamine, and lowest for MDMA, MDEA, and hydrocodone. Furthermore, the estimated per capita consumption of fentanyl was higher than previous reports from rural and university settings in the U.S. Generally, drug consumption was higher during the spring semesters, with year-on-year semester increases also noted over the 2-y study period. Except for methadone and cocaine, the estimated average per capita consumption of drugs increased over the pandemic period, with the highest increase noted for MDMA (286 % increase compared to baseline, p = 0.016). Estimated average consumption of methadone and cocaine decreased slightly by 6 % and 7 %, respectively. These results demonstrate the utility and strength of wastewater-based approaches in capturing long-term and evolving trends in drug use within communities. Our study findings reflect the regionwide problem with opioid-related overdoses and increasing stimulant prescription rates. Our findings also provide objective data and insights for health policymakers on the effects of the pandemic period on community drug use in a rural U.S. town.

Effects of sewer biofilm on the degradation of drugs in sewage: A microcosm study.

A thorough understanding of the in-sewer stability of chemical biomarkers is critical in applying wastewater-based surveillance of community drug use. In this study, we examined the effects of sewer biofilm on the degradation of commonly abused drugs, namely, morphine, fentanyl, cocaine, and amphetamine, in wastewater using 48-h batch degradation tests. The experiments were designed to distinguish among abiotic, biochemical, and physical degradation processes, and used mature biofilm obtained from an actual sewer line. Parallel microcosm tests were conducted using wastewater with and without suspended biofilm. Results indicate that first order kinetics describe the degradation of the drugs in both wastewater and wastewater-biofilm microcosms. Amphetamine was most stable in all microcosms, with a maximum removal of only 34% after 48 h. Abiotic chemical transformation played a major role in the degradation of morphine (kab = 0.018 h-1), fentanyl (kab = 0.022 h-1) and cocaine (kab = 0.049 h-1) in wastewater. Fentanyl removal from wastewater was also influenced by the presence of biofilm (kf = 0.015 h-1). This study is the first to report on the effect of sewer biofilm on fentanyl degradation, and highlights the need to account for in-sewer drug stability in wastewater-based drug use estimation, particularly for chemicals with high affinity for organics.

Pandemic wave trends in COVID-19 cases, mobility reduction, and climate parameters in major metropolitan areas in the United States.

In this paper, we analyzed the association among trends in COVID-19 cases, climate, air quality, and mobility changes during the first and second waves of the pandemic in five major metropolitan counties in the United States: Maricopa in Arizona, Cook in Illinois, Los Angeles in California, Suffolk in Massachusetts, and New York County in New York. These areas represent a range of climate conditions, geographies, economies, and state-mandated social distancing restrictions. In the first wave of the pandemic, cases were correlated with humidity in Maricopa, and temperature in Maricopa and Los Angeles. In Suffolk and New York, cases were correlated with mobility changes in recreation, grocery, parks, and transit stations. Neither cases nor death counts were strongly correlated with air quality. Periodic fluctuations in mobility were observed for residential areas during weekends, resulting in stronger correlation coefficients when only weekday datasets were included in the analysis. We also analyzed case-mobility correlations when mobility days were lagged, and found that the strongest correlation in the first wave occurred between 12 and 14 lag days (optimal at 13 days). There was stronger but greater variability in correlation coefficients across metropolitan areas in the first pandemic wave than in the second wave, notably in recreation areas and parks. In the second wave, there was less variability in correlations over lagged time and geographic locations. Overall, we did not find conclusive evidence to support associations between lower cases and climate in all areas. Furthermore, the differences in cases-mobility correlation trends during the two pandemic waves are indicative of the effects of travel restrictions in the early phase of the pandemic and gradual return to travel routines in the later phase. This study highlights the utility of mobility data in understanding the dynamics of disease transmission. It also emphasizes the criticality of timeline and local context in interpreting transmission trends. Mobility data can capture community response to local travel restrictions at different phases of their implementation and provide insights on how these responses evolve over time alongside disease trends.

Wastewater surveillance for SARS-CoV-2 on college campuses: Initial efforts, lessons learned and research needs.

Background: Wastewater surveillance for SARS-CoV-2 is an emerging approach to help identify the risk of a COVID-19 outbreak. This tool can contribute to public health surveillance at both community (wastewater treatment system) and institutional (e.g., colleges, prisons, nursing homes) scales. Objectives: This research aims to understand the successes, challenges, and lessons learned from initial wastewater surveillance efforts at colleges and university systems to inform future research, development and implementation. Methods: This paper presents the experiences of 25 college and university systems in the United States that monitored campus wastewater for SARS-CoV-2 during the fall 2020 academic period. We describe the broad range of approaches, findings, resource needs, and lessons learned from these initial efforts. These institutions range in size, social and political geographies, and include both public and private institutions. Discussion: Our analysis suggests that wastewater monitoring at colleges requires consideration of information needs, local sewage infrastructure, resources for sampling and analysis, college and community dynamics, approaches to interpretation and communication of results, and follow-up actions. Most colleges reported that a learning process of experimentation, evaluation, and adaptation was key to progress. This process requires ongoing collaboration among diverse stakeholders including decision-makers, researchers, faculty, facilities staff, students, and community members.

Wastewater Surveillance for SARS-CoV-2 on College Campuses: Initial Efforts, Lessons Learned, and Research Needs.

Wastewater surveillance for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging approach to help identify the risk of a coronavirus disease (COVID-19) outbreak. This tool can contribute to public health surveillance at both community (wastewater treatment system) and institutional (e.g., colleges, prisons, and nursing homes) scales. This paper explores the successes, challenges, and lessons learned from initial wastewater surveillance efforts at colleges and university systems to inform future research, development and implementation. We present the experiences of 25 college and university systems in the United States that monitored campus wastewater for SARS-CoV-2 during the fall 2020 academic period. We describe the broad range of approaches, findings, resources, and impacts from these initial efforts. These institutions range in size, social and political geographies, and include both public and private institutions. Our analysis suggests that wastewater monitoring at colleges requires consideration of local information needs, sewage infrastructure, resources for sampling and analysis, college and community dynamics, approaches to interpretation and communication of results, and follow-up actions. Most colleges reported that a learning process of experimentation, evaluation, and adaptation was key to progress. This process requires ongoing collaboration among diverse stakeholders including decision-makers, researchers, faculty, facilities staff, students, and community members.

Decision-making in irrigation networks: Selecting appropriate canal structures using multi-attribute decision analysis.

The stiff competition for water between agriculture and non-agricultural production sectors makes it necessary to have effective management of irrigation networks in farms. However, the process of selecting flow control structures in irrigation networks is highly complex and involves different levels of decision makers. In this paper, we apply multi-attribute decision making (MADM) methodology to develop a decision analysis (DA) framework for evaluating, ranking and selecting check and intake structures for irrigation canals. The DA framework consists of identifying relevant attributes for canal structures, developing a robust scoring system for alternatives, identifying a procedure for data quality control, and identifying a MADM model for the decision analysis. An application is illustrated through an analysis for automation purposes of the Qazvin irrigation network, one of the oldest and most complex irrigation networks in Iran. A survey questionnaire designed based on the decision framework was distributed to experts, managers, and operators of the Qazvin network and to experts from the Ministry of Power in Iran. Five check structures and four intake structures were evaluated. A decision matrix was generated from the average scores collected from the survey, and was subsequently solved using TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method. To identify the most critical structure attributes for the selection process, optimal attribute weights were calculated using Entropy method. For check structures, results show that the duckbill weir is the preferred structure while the pivot weir is the least preferred. Use of the duckbill weir can potentially address the problem with existing Amil gates where manual intervention is required to regulate water levels during periods of flow extremes. For intake structures, the Neyrpic® gate and constant head orifice are the most and least preferred alternatives, respectively. Some advantages of the Neyrpic® gate are ease of operation and capacity to measure discharge flows. Overall, the application to the Qazvin irrigation network demonstrates the utility of the proposed DA framework in selecting appropriate structures for regulating water flows in irrigation canals. This framework systematically aids the decision process by capturing decisions made at various levels (individual farmers to high-level management). It can be applied to other cases where a new irrigation network is being designed, or where changes in irrigation structures need to be identified to improve flow control in existing networks.

Occurrence, distribution, and sources of antimicrobials in a mixed-use watershed.

The release into the environment of antimicrobial compounds from both human and agricultural sources is a growing global concern. The Grand River watershed, the largest mixed-use watershed in southern Ontario, receives runoff from intensive animal production as well as municipal wastewater effluents from a rapidly increasing human population. A survey of surface waters and wastewater effluents was conducted across the watershed to assess the occurrence and distribution of several antimicrobials (i.e., trimethoprim, sulfamethoxazole, sulfamethazine, lincomycin, and monensin) and chemical indicators (i.e., ammonia, nitrate, ibuprofen, venlafaxine, atrazine) and to characterize exposure levels. The human antimicrobials trimethoprim and sulfamethoxazole were detected in the urban areas of the main channel at mean concentrations of 8 ± 7 ng/L and 31 ± 24 ng/L, respectively, but at much lower concentrations in the agricultural tributaries. In contrast, the veterinary antimicrobial sulfamethazine was detected at a mean concentration of 11 ± 9 ng/L in the main channel, and at a much higher concentration in the agricultural tributaries. Lincomycin was detected in only two river samples and not in the effluents while monensin was not detected in all samples. The herbicide atrazine was detected at very low concentrations in the surface waters of both the tributaries and the main channel. The concentrations of the antimicrobials and chemical indicators generally increased downstream of the confluences with agricultural tributaries and effluent outfalls. In the wastewater effluents, the concentrations of trimethoprim, sulfamethoxazole, ibuprofen, and venlafaxine decreased with increasing treatment levels (i.e., secondary to tertiary) as indicated by ammonia/nitrate concentrations. There was a strong correlation among trimethoprim, sulfamethoxazole, and venlafaxine in the main channel and in the wastewater effluents. While the environmental concentrations of antimicrobials in the watershed are low relative to toxicity thresholds, their persistence in the environment may be an important consideration in defining strategies for future water management.

Predicting EDC concentrations in a river mixing zone.

The fate and transport of endocrine disrupting chemicals (EDCs) in ambient river waters is a major concern associated with effluents from municipal wastewater treatment plants (WWTPs). This paper presents a methodology for quantifying the spatial distribution of EDCs in a river mixing zone. The core of the technical analysis is based on a two-dimensional steady-state analytical model characterized by ambient turbulence in the receiving water. This model was first calibrated with mass transport data from field measurements for a conservative substance (electrical conductivity) and then used to predict aqueous-phase EDC concentrations throughout a WWTP mixing zone. To demonstrate the usefulness of this methodology for water quality management purposes, the modeling framework presented in this paper was used to determine a lumped in-stream attenuation rate constant (k(d)=3 d(-1)) for 17ß-estradiol under natural conditions. This rate constant likely accounts for the combined contributions of physical sorption, photolysis, microbial and chemical degradation, and the measured value is highly consistent with previously published results from bench-scale removal experiments.