Supply Uncertainty Research Articles

A graph-theory-based dynamic programming planning method for distributed energy system planning: Campus area as a case study

Distributed energy systems are widely used in current regional energy planning because of their flexibility in terms of energy supply. However, the differentiated demand for various building loads increases the uncertainty of the energy supply. To reduce load fluctuation and hydraulic imbalance, a dynamic programming method based on graph theory was proposed in this study for energy station site selection and pipeline network layout deployment. The kernel density method was applied to distribute the regional building load for the site selection of energy stations. With the minimum load fluctuation rate as the goal, a 0–1 dynamic programming method was proposed to optimize the energy supply range of the energy station. Based on graph theory, an improved Prim algorithm was developed to determine the pipeline network layout. Taking a campus area as a case study, the proposed planning method was shown to reduce the initial investment, annual operating cost, and equivalent annual cost by 1.23%, 6.52%, and 5.04%, respectively. The optimized planning scheme not only balanced the load fluctuation in each energy station but also reduced the total pressure loss of the pipeline network by 19.86%.

Chance-constrained bi-level optimal dispatching model and benefit allocation strategy for off-grid microgrid considering bilateral uncertainty of supply and demand

Off-grid microgrids lack of backup of the main network, which poses a higher challenge to the effective energy supply, how to improve the matching degree of supply and demand has become a popular direction of current research. Therefore, firstly, on the basis of considering the bilateral uncertainty of supply and demand, based on the demand response model, a chance-constrained bi-level optimal dispatching model is constructed to promote the linkage of “demand-supply”; Then, considering that the demand side participation in the demand response on the energy supply side can bring incremental benefits, an benefit allocation strategy for both sides of supply and demand is constructed; Finally, an off grid microgrid in Northwest China is selected as the research background, using scenario generation and opportunity constraints to deal with uncertainty, and using an improved genetic algorithm to solve the bi-level optimal dispatching model. The results show that: The proposed two-level uncertainty processing model can effectively reduce the energy supply deviation; The optimal dispatching model proposed can realize “demand-supply” interaction and reduce the load shedding rate; The installed capacity of units can be increased to further reduce the load shedding, but it will cause higher curtailment of clean energy, consideration can be given to implementing demand response or increasing energy conversion units to enhance efficiency of clean energy utilization; The improved Shapley value method can improve the willingness of the demand side to actively participate in the demand response of the supply side, and improve the satisfaction of the cooperative alliance.

Rare Earth Element (REE) and Critical Mineral Fractions of Central Appalachian Coal-Related Strata Determined by 7-Step Sequential Extraction

Rare earth elements (REEs) and critical minerals (CMs) are used in many modern industries, including the automotive sector, generation and storage, clean energy, and defense. The demand for REEs is increasing, and the REE supply chain is unpredictable. The US has driven to assess non-conventional sources of REE (such as coal underclay) to identify domestic resources to stabilize this uncertainty in supply. Knowledge of the minerology, distribution, and modes of occurrence of REEs is integral to the assessment of non-conventional sources. Additionally, extraction techniques can be optimized and targeted when REE distribution in different solid fractions from source material is understood. In this study, four bituminous coal-related samples associated with the Lower and Middle Kittanning coal seams in the Appalachian Basin (US) underwent a seven-step sequential extraction procedure, primarily targeting the water-soluble, exchangeable, acid soluble, mildly reducible, moderately reducible, strongly reducible, and oxidizable fractions. The REE and other elements of interest from each extraction step were analyzed, and the percentages of element extracted from raw solids were calculated. REEs extracted from the total seven steps were reported as the extractable fraction, whereas the fractions in the residual solids were reported as the non-extractable fraction. Less than 6% of REE were extracted from three samples. Twenty-one percent of REE was extracted from the fourth sample, mainly from the steps targeting oxidizable and exchangeable phases. Co-extraction of critical metals (Co, Ni, Cu, and Zn) occurred during the oxidizable, exchangeable, acid soluble, and water-soluble steps for the four samples. In the extracted fractions, the four samples all exhibited a middle and heavy REE enrichment relative to light REE. The mobility of major cation (e.g., Ca, Fe, and P) and REE is associated with exchangeable, oxidizable, and acid soluble fractions. Non-extractable REE is likely held in refractory apatitic phases, and/or primary REE-phosphates (e.g., monazite and xenotime).

A quantitative reliability assessment and risk quantification method for microgrids considering supply and demand uncertainties

Microgrids have received great interest in achieving high renewable energy penetration. However, intermittent renewable generation may cause reliability problem (i.e., power inadequacy), especially for islanded microgrids. A comprehensive quantitative reliability assessment of microgrids considering uncertainties is essential. Current uncertainty-based reliability assessment approaches usually consider the uncertainties on the supply side only or additionally consider the uncertainties on the demand side using a general distribution without considering the uncertainty characteristics of different demands. This may lead to unreliable assessment results. Besides, existing reliability performance indexes cannot measure the probability/risk of power inadequacy under uncertainties. In this study, a novel uncertainty-based reliability assessment approach is therefore developed for microgrids considering uncertainties at both supply and demand sides. A new reliability index is proposed and a risk quantification method is developed to measure the risk/probability of power inadequacy under uncertainties. The uncertainties at both supply and demand sides are detailedly quantified using a bottom-up approach. The proposed reliability assessment and risk quantification method are tested on an islanded hotel microgrid in Hong Kong. The results show that the proposed reliability assessment approach can provide more robust reliability assessment results while the conventional approach has a probability of 8% to underestimate the maximum outage power. The proposed risk quantification method can help to provide more comprehensive reliability assessment results under uncertainties. The risk quantification results indicate that the highest monthly power inadequacy risk of the hotel microgrid appears in August (i.e., 5.8%), while the highest hourly risk appears at 9:00 p.m. (i.e., 4.8%).

Risk-based multistage stochastic mixed-integer optimization for biofuel supply chain management under multiple uncertainties

CO2 emissions from the transportation sector account for 22% of global emissions. Blending biofuels with fossil fuels is a promising method for reducing these emissions and ensuring energy security. One of the most important and challenging aspects of the biofuel production process is the design of supply chains that integrate various types of biomasses under multiple uncertainties. The aim of this study is to address this issue by proposing a multi-stage stochastic program for strategic and tactical planning in multi-feedstock biofuel supply chain networks under uncertainties of biomass supply and biofuel demand. The proposed model minimizes the total annualized cost by selecting raw materials, positioning production facilities, placing storage facilities, and defining deferral paths for each feedstock. In addition, a new dynamic method of the conditional value at risk is adopted to consider the risk hedge. Deterministic, stochastic, and risk-based models are compared, and case studies based on the South Korean context show the effectiveness of the model and the algorithm. Computational results indicate that considering risk can increase biofuel expected production cost by up to 88%. It also shows that biomass transportation is the most affected part of the supply chain in risk assessment. Furthermore, it indicates that a value of 0.5 is the most effective risk aversion factor.

Empirical Safety Stock Estimation Using GARCH Model, Historical Simulation, and Extreme Value Theory: A Comparative Study

Safety stock (SS) is an appropriate tactic to deal with demand and supply uncertainty with the aim of preventing inventory shortages. In the literature, previous work on SS estimation assumes that the forecast error distributions (FED) are independent and identically distributed (i.i.d) following the normal distribution. In order to assess violations of this assumption, there are many solution methods in the recent literature that include the following: (1) Consider the FED as other distribution models, such as gamma distribution or log-normal distribution, etc. (2) Use the Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) model to consider the Heteroskedasticity phenomena, (3) Use the extreme value theory (EVT) to take into consideration the occurrence of extreme demands, etc. However, the performance of these methods is not guaranteed because there is an absence of comparative studies. Indeed, the estimation of SS is based on the approximation of quantiles of the FED. Such quantiles are related to the cycle service levels (CSL) that are important to achieve company goals. Accordingly, the aim of this research is to propose two combined empirical methods to determine the SS in a more robust fashion and compare them with traditional methods under different supply chain parameters. The first combined method, named Filtered Historical Simulation (FHS), consists of combining the GARCH model with the simulation method. The second combination named Conditional Extreme Value Theory (CEVT) is the GARCH model with EVT. To validate these proposed combined methods, the SS is also estimated using traditional methods, such as simple exponential smoothing (SES), simulation, and kernel density estimation (KDE). The methodology is illustrated with both simulation data and real case study data for different lead times. For the FED, two cases are studied: lognormal distribution and gamma distribution. The results show the superiority of the two proposed combination methods with respect to the tick loss function (TLF) for the different CSL targets and for shorter and longer lead times. Results are confirmed using the ANOVA test.

Addressing supply uncertainties using multi-period stochastic economic evaluation: A graph-theoretic aided element targeting approach

The challenges to commercialize biomass industry includes biomass supply shortage which is dependent on geographical location and seasonality. Most of the present biomass supply chain studies had not considered incorporation of supply chain uncertainties that may lead to overestimation of financial performance. Therefore, a hybrid framework was proposed via integration of stochastic Monte Carlo Simulation model with element targeting approach (Biomass Element Life Cycle Analysis, BELCA-P-graph model) to perform scheduling and economic analysis for the biomass supply chain. The BELCA-P-graph model aimed to generate a baseline for the feedstock ratio of each input biomass. This was then input into the stochastic model, capable of estimating the financial probability of the supply chain while incorporating supply chain uncertainties (i.e., biomass element characteristics, transportation-related parameters, raw material pricing, biomass availability, market demand, and selling price of final product). Results showed that biomass shortage had decreased the mean Net Present Value (NPV) of the base case scenario (without consideration biomass supply shortage) by 1.39%–12.21%. Storage capacity consideration had decreased the mean NPV by 11.59%–12.21%. The sensitivity analysis found that syngas demand and syngas selling price uncertainty offered significant impact on the mean NPV outcome. • Integrated Monte Carlo model with BELCA-P-graph model was presented. • Six supply chain uncertainties are modelled using Monte Carlo simulation. • Biomass shortage reduced mean NPV of base case up to 12.21%. • Implementation of storage resulted in a further reduction in mean NPV.

Multi-energy dispatching for uncertainty EV demand: A simulation approach

Uncertainty and randomness in demand and supply bring significant challenges to the stable operation of the grid and the scheduling planning of multi-energy sources. To solve these challenges, we propose and analyze a multi-energy dispatching model which minimizes the total cost and enhances the efficiency of supplying power. Specifically, we design matching algorithms that simulate an appropriately scaled sequence of stochastic EV demand. We also analyze four different energy dispatching scenarios proving that the scheduling model and the multi-energy synergistic microgrid structure can bring higher efficiency and lower costs. Our main contribution is using a simulation approach to take EVs into account for demand-side uncertainty, which significantly improves the efficiency of grid dispatch.

How does strategic flexibility make a difference for companies? An example of the Hungarian food industry

Nowadays, strategic flexibility and its effect on organizational performance are crucial to discuss. Moreover, organizations, especially industrial companies, should estimate how flexibility as a mechanism can improve organizational performance. The Hungarian food industry is highly significant in the industrial sector of the Hungarian economy. Therefore, the aim of this paper is to evaluate how the performance of the Hungarian food industry is affected by strategic flexibility, using supply and demand uncertainty as moderators. It is a quantitative and causal study. A survey was conducted to collect the primary data from a proposed sample of managers at the target companies. As a result, 301 valid responses have been analyzed in SPSS. Regression analysis, correlation, and moderation analysis are used as well. The results indicated that strategic flexibility generally enhances the performance of the target companies, and 20.3% of changes in companies’ performance are related to strategic flexibility. The flexibility of resources affects only the operational performance, while the flexibility of coordination positively affects company performance; it has a 44.2% influence. The findings also showed that uncertainty does not moderate the relationship between strategic flexibility and target firms’ performance. Thus, strategic flexibility is considered as one-effect mechanism in a stable business environment. In all cases, strategic flexibility should be applied in addition to other managerial techniques to enhance company performance.

Risk Identification Techniques In Retail Industry: A Case Study Of Tesco Plc

Purpose: The prime purpose of this paper is to analyze the risks associated with the retail industry and provide a overview of the risk identification techniques and tools. The paper also investigates the relationship between risk identification and risk management. Research Methodology: This paper is a conceptual paper and the methodology included a literature search. The present study is a literature review that examines available studies based on risk identification and its techniques in the retail business. Findings:Many risks have a high impact on the retail industry. Supplier uncertainty, change in the taste and preference of consumers, data security and threats, inventory risks, etc. all these types of risks are faced by the retail industry. Identifying these risks is a big challenge for the company. There are numerous risk identification techniques that companies can use to identify risks. Brainstorming, expert judgment, Delphi method, root-cause analysis, etc. techniques are discussed in this paper. Tesco plc applies the brainstorming, risk register and top-down and bottom-up approach to identify the risks. The findings of this study can be used as a case study of Tesco's retail operation, which is based in and managed primarily in this specific area. Tesco's decisions may contain some common tactics for identifying hazards, and some extremely unique ones. Practical Implications: Various corporations developed risk departments to identify and manage risks during the modernization and automation era. As a result, there is a demand for experts with specialized skills and experience in managing and assessing risk factors involved in the retail industry. These specialists' job is to identify hazards and develop methods to protect the organization from them. This study will assist the risk managers or professionals of the retail industry to use the techniques of risk identification and assess the risks appropriately.

Antecedents of agriculture supply chain performance during COVID-19: an emerging economy perspective

COVID-19 led to several complications like labor shortage, inadequate availability of supply chain facilities, price fluctuation, panic behavior, and uncertainty. Despite the challenges, the farmers and stakeholders adopted different strategies for sustaining the agricultural supply chain performance. Hence, the study proposes to explore the antecedents that led to the ASC performance and their linkages. The study combines literature review and qualitative methods to understand and recognize antecedents that impact ASC performance in developing countries. The conceptual model is built with the underpinning theory (the theory of commitment and organization). A quantitative survey follows the study to validate the theoretical model empirically using covariance-based structure equation modeling (AMOS). The ASC performance in the earlier phase of COVID-19 was disrupted. With time, ASC performance was managed by linking different antecedents. The findings reveal the application and linkage of organization theory and theory of commitment which led to ASC performance. The study finds a unique contribution among academia and practitioners by paving directions to manage the uncertainty during COVID-19 and any such risk triggers in the future. The study offers essential insights for practitioners and policymakers to develop a road map for ASC during the calamities.

Design, Implementation, and Evaluation of an Output Prediction Model of the 10 MW Floating Offshore Wind Turbine for a Digital Twin

Predicting the output power of wind generators is essential to improve grid flexibility, which is vulnerable to power supply variability and uncertainty. Digital twins can help predict the output of a wind turbine using a variety of environmental data generated by real-world systems. This paper dealt with the development of a physics-based output prediction model (P-bOPM) for a 10 MW floating offshore wind turbine (FOWT) for a digital twin. The wind power generator dealt with in this paper was modeled considering the NREL 5 MW standard wind turbine with a semi-submersible structure. A P-bOPM of a 10 MW FOWT for a digital twin was designed and simulated using ANSYS Twin Builder. By connecting the P-bOPM developed for the digital twin implementation with an external sensor through TCP/IP communication, it was possible to calculate the output of the wind turbine using real-time field data. As a result of evaluating the P-bOPM for various marine environments, it showed good accuracy. The digital twin equipped with the P-bOPM, which accurately reflects the variability of the offshore wind farm and can predict the output in real time, will be a great help in improving the flexibility of the power system in the future.

Advance selling under uncertain supply and demand: a robust newsvendor perspective

AbstractConsidering the restriction of supply risk on optimal profit realization in advance selling, we discuss three selling strategies of a seller who produces and sells a seasonal product to a consumer under uncertain supply and demand: all advance selling, partial advance selling, and non‐advance selling. The robust newsvendor model is designed to solve the problem. Our results show that implementing an advance selling strategy is always beneficial from the demand uncertainty perspective to the seller. However, sellers should choose advance selling carefully from the standpoint of supply uncertainty: sellers will non‐advance selling under certain conditions. This condition is contingent on the market, capacity level, selling price, supply–demand correlation, consumer characteristics, and seller's pricing power. Interestingly, pricing power is the key driver to stimulate advance selling under supply uncertainty. In addition, the impact of supply and demand uncertainty and supply–demand correlation on these strategies are related to price discounts.

Multi-time scale energy management of microgrid considering the uncertainties in both supply and demand

The uncertainty of supply and demand have different characteristics, which can adversely affect the stable operation of microgrid. This paper presents a multi-time scale scheduling strategy, which mainly consists of day-ahead scheduling layer and intraday adjustment layer. In the day-ahead scheduling layer, the Monte Carlo method and demand response are used to deal with the uncertainty of both supply and demand, and a stochastic optimal model is established by considering the uncertainty in the microgrid. In the first stage, the unit commitment decisions are derived in a stochastic optimization framework to achieve minimum operating cost. In the second stage, the day-ahead economic power scheduling is achieved by using a deterministic optimization framework. In the intraday adjustment layer, a rolling optimization algorithm is applied to adjust the day-ahead economic power scheduling plan, which does not change the unit commitment decisions. The power fluctuations caused by inaccurate day-ahead forecast data are effectively smoothed with intraday rolling optimization. The simulation results show that the proposed model and strategy can effectively reduce the impact of uncertainty on the optimal scheduling of the microgrid. In addition, comparing the scheduling results for one month, it can be seen that the proposed strategy has better economy in the long-time operation of the microgrid.

A two-stage decision-support approach for improving sustainable last-mile cold chain logistics operations of COVID-19 vaccines.

The COVID-19 pandemic has become a global health and humanitarian crisis that catastrophically affects many industries. To control the disease spread and restore normal lives, mass vaccination is considered the most effective way. However, the sustainable last-mile cold chain logistics operations of COVID-19 vaccines is a complex short-term planning problem that faces many practical challenges, e.g., low-temperature storage and transportation, supply uncertainty at the early stage, etc. To tackle these challenges, a two-stage decision-support approach is proposed in this paper, which integrates both route optimization and advanced simulation to improve the sustainable performance of last-mile vaccine cold chain logistics operations. Through a real-world case study in Norway during December 2020 and March 2021, the analytical results revealed that the logistics network structure, fleet size, and the composition of heterogeneous vehicles might yield significant impacts on the service level, transportation cost, and CO2 emissions of last-mile vaccine cold chain logistics operations.

Energy flexibility and viability enhancement for an ocean-energy-supported zero-emission office building with respect to both existing and advanced utility business models with dynamic responsive incentives

The foreseeable large-scale deployment of intermittent renewable energy systems in the future and fluctuations in energy markets can severely affect the operation and stability of smart grids, creating significant uncertainty and instability in the electricity supply and demand. It is believed that the mismatch between energy demand of a building and instantaneous renewable energy generation can be reduced by controlling energy use through the energy flexibility of the respective building. Enhancing the energy flexibility of a zero-emission building can satisfy the demands of the energy network around the building and contribute to the resilience of the energy system. Meanwhile, in an electricity tariff model with a distinction between peak and off-peak periods, enhancing energy flexibility in buildings can reduce the operating costs of electricity consumption, with benefits to the economic performance of the building itself. In this study, a simulated hypothetical zero-emission office building near the coast with a floating photovoltaic system and tidal stream generator system in Hong Kong from previous study were used in a case study to investigate the impact of energy flexibility control with stationary batteries as the source of energy flexibility. Two flexibility control strategies were designed to demonstrate their impact on the economic performance of the system. After incorporating the “Peak Demand Management” (PDM) programme in Hong Kong, variations in the economic performance of the system were demonstrated through simulations. Considering the incentive provided by the PDM programme, the possibility of achieving a neutral economic performance at different percentages of renewable energy generation over a 20-year life cycle without the feed-in tariff was investigated. Furthermore, two possible modifications for business models and the PDM programme are proposed. The simulation results indicate that both suggestions can significantly improve the economic performance of the system.

Attended home delivery under uncertain travel and response time: a case of Indian public distribution system

PurposeBeing the final end of the logistic distribution, attended home delivery (AHD) plays an important role in the distribution network. AHD typically refers to the service provided by the distribution service provider to the recipient's doorstep. Researchers have always identified AHD as a bottleneck for last-mile delivery. This paper addresses a real-life stochastic multi-objective AHD problem in the context of the Indian public distribution system (PDS).Design/methodology/approachTwo multi-objective models are proposed. Initially, the problem is formulated in a deterministic environment, and later on, it is extended to a multi-objective AHD model with stochastic travel and response time. This stochastic AHD model is used to extensively analyze the impact of stochastic travel time and customer response time on the total expected cost and time-window violation. Due to the NP-hard nature of the problem, an ant colony optimization (ACO) algorithm, tuned via response surface methodology (RSM), is proposed to solve the problem.FindingsExperimental results show that a change in travel time and response time does not significantly alter the service level of an AHD problem. However, it is strongly correlated with the planning horizon and an increase in the planning horizon reduces the time-window violation drastically. It is also observed that a relatively longer planning horizon has a lower expected cost per delivery associated.Research limitations/implicationsThe paper does not consider the uncertainty of supply from the warehouse. Also, stochastic delivery failure probabilities and randomness in customer behavior have not been taken into consideration in this study.Practical implicationsIn this paper, the role of uncertainty in an AHD problem is extensively studied through a case of the Indian PDS. The paper analyzes the role of uncertain travel time and response time over different planning horizons in an AHD system. Further, the impact of the delivery planning horizon, travel time and response time on the overall cost and service level of an AHD system is also investigated.Social implicationsThis paper investigates a unique and practical AHD problem in the context of Indian PDS. In the present context of AHD, this study is highly relevant for real-world applications and can help build a more efficient delivery system. The findings of this study will be of particular interest to the policy-makers to build a more robust PDS in India.Originality/valueThe most challenging part of an AHD problem is the requirement of the presence of customers during the time of delivery, due to which the probability of failed delivery drastically increases if the delivery deviates from the customer's preferred time slot. The paper modelled an AHD system to incorporate uncertainties to attain higher overall performance and explore the role of uncertainty in travel and response time with respect to the planning horizon in an AHD, which has not been considered by any other literature.

Congestion tolling — Dollars versus tokens: Within-day dynamics

Tradable credit schemes (or tolling in tokens) are a form of quantity control, which promise to be an appealing alternative to congestion pricing (or tolling in dollars) owing to considerations of revenue neutrality, equity, reduced infrastructure costs, and political acceptability. The comparative performance of the two instruments under uncertainty in demand and supply has only recently received attention in the transportation setting, despite being widely studied for emission markets. In this paper, we add to this literature by considering a tradable credit scheme in a departure time context wherein users are provided an initial endowment of tokens by the regulator and incur a token charge (determined prior to all departures) to travel in a specific time-period. Tokens can be bought and sold within a marketplace at a price determined endogenously by token demand and supply. Two key features of the market model are: (1) the time-of-day dynamics of price is explicitly modeled through a smooth market clearing mechanism in each period, and (2) the selling decisions of users, which determine the distribution of token supply in the market over the day are explicitly modeled. This enables us to study the impact of selling behavior on performance of the credit system. Travel demand is modeled using a logit mixture model and supply consists of static congestion.Extensive experiments under stochastic demand show that when the tolls (in dollars and tokens) are not day-to-day adaptive, tolling in tokens outperforms tolling in dollars when congestion effects are more severe (e.g., realistic BPR models and steep congestion functions, high demand levels and high day-to-day variability). Importantly, we find that this result is robust with respect to selling behavior in the market, although there can be welfare losses in the quantity control system when selling behavior in the market is excessively irrational. These findings underscore the importance of examining disaggregate market behavior when designing tradable credit schemes. Moreover, when the supply of tokens can be adapted from day to day, the credit system was found to be superior in all tested scenarios, provided the selling behavior of individuals is rational. Finally, even in the case when toll revenues in the price instrument are equally redistributed (often difficult in practice), tolling in tokens (when tokens are equally distributed) is marginally more equitable in scenarios where congestion effects are more severe. These findings make a case for tolling in tokens.

Changes in Canada's Phosphorus Cycle 1961–2018: Surpluses and Deficits

AbstractHuman activities have greatly changed global phosphorus (P) cycling, posing urgent challenges related to both supply uncertainty and aquatic eutrophication. However, the long‐term dynamics of P across Canada remain unquantified and under‐explored. Using a material flow analysis model, we quantified temporal dynamics of P cycling in Canadian provinces from 1961 to 2018 and characterized the changes in soil P balances through the study period. We found most Canadian agricultural regions had soil P surpluses except Saskatchewan, where large P deficits (−10‐0 kg ha−1 y−1) were detected in almost all study years (except 1961). In 2018, Quebec and Atlantic provinces had the highest P surpluses (34 and 159 kg ha−1 y−1, respectively), and low P surpluses were observed in Ontario, Manitoba, Alberta and British Columbia (9, 16, 5, and 28 kg ha−1 y−1, respectively). P surplus was reduced in Quebec and Ontario after the nutrient management regulations were put in place in the 1980s. We demonstrated that P flows in cropland played a larger role in Canada P cycling than pasture. P use efficiency tended to be greatest in the Prairie provinces (0.74 in 2018), and least in the Atlantic provinces (0.12 in 2018). However, the rate of increase was considerably steeper in Ontario and Quebec than other provinces. Reducing inorganic fertilizer and manure application would be the most effective method to reduce remaining P surpluses.

Knowledge, attitudes, and behaviors related to the fentanyl-adulterated drug supply among people who use drugs in Oregon

IntroductionNonpharmaceutical fentanyl has reconfigured the U.S. illicit drug market, contributing to a drastic increase in overdose drug deaths. While illicit fentanyl has subsumed the drug supply in the Northeast and Midwest, it has more recently reached the West. For this study, we explored knowledge, attitudes, and behaviors among people who use drugs in Oregon in the context of the emergence of fentanyl in the drug supply. MethodsWe conducted in-depth interviews by phone with 34 people who use drugs in Oregon from May to June 2021. We used thematic analysis to analyze transcripts and construct themes. ResultsPeople who use drugs knew about fentanyl, expressed doubt that fentanyl could be found in methamphetamine; believed those who were younger or less experienced were at higher risk for harm; and received information about fentanyl from drug dealers, syringe service programs, or peers (other people who use drugs). Preference for fentanyl's presence in drugs like heroin or methamphetamine was mixed. Some felt that their preference was irrelevant since fentanyl was unavoidable. Participants reported engaging in harm reduction practices, including communicating about fentanyl with dealers and peers, testing for fentanyl, using smaller quantities of drugs, switching from injecting to smoking, and using naloxone. ConclusionPeople who use drugs are responding to the rise of fentanyl on the West Coast and are concerned about the increasing uncertainty and hazards of the drug supply. They are willing and motivated to adopt harm reduction behaviors. Harm reduction promotion from syringe service programs and public health agencies is essential to reduce injury and death from nonpharmaceutical fentanyl.