Prioritize Mitigation Strategies Research Articles

Sustainable supply chain control strategies for the natural stone market

The natural stone market is an important component of the modern economy, with increasing demand driven by a growing population and urbanization. However, the industry faces a range of challenges related to supply chain risk management, including sourcing raw materials, transportation, and quality control. Effective risk management is crucial for the success of any business in this sector, as it helps to minimize losses, maintain quality, and ensure timely delivery of products to customers. This paper proposes the use of a flexible decision-making approach to classify major risks and identify Effective mitigation strategies for order management in the natural stone market. The proposed method considers multiple criteria, including financial, operational, and mitigation factors, to develop a comprehensive risk assessment framework that can be used to optimize control processes. By implementing the proposed framework, stakeholders in the natural stone market can improve their risk management capabilities and enhance their competitive advantage in the global market. The framework can help to identify the most critical risks and prioritize mitigation strategies, resulting in a more efficient and Effective supply chain management system. This paper presents a real case study of a Brazilian company, which illustrates the practical application of the proposed methodological framework in the natural stone market, highlighting its Effectiveness in identifying and mitigating risks related to stone slabs transportation network. The insights gained from this case study have potential for application across various industries facing analogous challenges in managing risks within their supply chains.

Carbon footprints, mitigation effects and economic performance of dairy farm systems in Inner Mongolia

CONTEXTReducing the GHG emissions intensity of milk production has become a key sustainability challenge for the dairy sector. Addressing this challenge requires thorough understanding of the current GHG emission levels of individual farms, the practices influencing GHG emissions, and the economic trade-offs associated with reducing those emissions. OBJECTIVEQuantify the carbon footprint (CF) and economic performance of milk production in Inner Mongolia (the main dairy farming region in China), and understand the main contributing factors to them. The effects of potential mitigation options at dairy farm system level were evaluated. METHODS: Life cycle assessment methodology was used to analyze the CF of 15 dairy farms (with year-round cow housing and crop feeding) selected for different milk production levels, herd structures and diet composition. RESULTS AND CONCLUSIONSThe average CF per kg of fat and protein corrected milk (FPCM; after allocation to milk) was 2.3 kg carbon dioxide equivalents (CO2-eq) (1.5–3.4 kg CO2-eq). Emissions related to feed production, manure management and enteric fermentation were the top three GHG emission sources and contributed an average of 27% (5%–46%), 28% (19%–38%) and 18% (14%–25%), respectively, to total emissions. The average cost of the farm system per ton FPCM was USD 540 per year. Feed costs dominated at 70% of the total costs. The CF of milk was negatively correlated with profit per kg FPCM. Mitigation potential of the CF of dairy production for single mitigation strategies ranged from 2.3% to 20.0% compared with business as usual, and combined mitigation measures could reduce emissions by 48.3%. The most effective mitigation measures should focus on decreasing N-fertilizer use per ha for feed crop production and increasing feed crop yield, adjusting herd structure for milk productivity and using localized feed inputs. All mitigation measures had a positive impact on farm profit, except for the improved manure management (changing the farms using manure composting to a static compost system and changing natural storage to direct land application). Therefore, the combination of crop and dairy production and manure management offer the greatest potential to reduce the GHG emissions from Inner Mongolia dairy farm systems. Economic subsidies and extension of findings to farmers might encourage more rapid adoption of these technologies. SIGNIFICANCEThe insights from our study can provide guidance for defining and prioritizing mitigation strategies for dairy production to reduce GHGs emission in other rapidly developing countries and regions with crop-based housed-cow dairy production.

Geospatial and statistical analysis of urban heat islands and thermally vulnerable zones in Bangalore and Hyderabad cities in India

Urban Heat Island (UHI) effects caused by urbanization and associated anthropogenic activities have adverse environmental and social impacts. The social impacts of UHIs on Indian cities are not well documented. To fill this void, the current paper examines the spatial variation of the UHI effects on the populations of two Indian cities: Bangalore and Hyderabad. Land Surface Temperature (LST) data for the past two decades is obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to analyse the spatial and temporal variation of the thermal environment of the cities. The regions experiencing different levels of UHI effects within the cities, along with the population in these zones, are identified in the study. The average UHI intensity for the past two decades in Bangalore and Hyderabad is found to be 1.9 °C and 2.44 °C, respectively. The UHI intensity in Bangalore and Hyderabad is found to be increasing annually at a rate of 0.040 °C per year and 0.033 °C per year, respectively. Around 1 million people in Bangalore and 1.8 million people in Hyderabad are inhabitants of the high temperate zone, experiencing an average LST of 3.14 °C and 3.55 °C higher than the rest of the population, respectively. The study emphasizes the need for regionally scaled UHI mitigation strategies and identifies the most vulnerable regions within the study area to prioritize mitigation strategies.

SIGeR-RV: A Web-Geographic Information System-Based System for Risk Management of Road Networks Exposed to Natural Hazards

The consequences of natural hazards are frequently estimated by the direct cost of recovering damaged infrastructure and the indirect costs to road users, economic activities, and impacts on society. Road networks are crucial in economic systems, logistic chain continuity, accessibility, mobility, and the evacuation of population during and after extreme events. Risk management systems (RMS) are used to estimate the potential consequences of natural events and to assess strategies for risk reduction. These commonly assess hazards, assets exposure, economic losses, and risk mitigation actions, among others. The general framework of RMS can be adapted to different scenarios. Still, local characteristics, such as the types of hazards and physical assets, cannot always be directly adopted from these systems. This study discusses the development of SIGeR-RV, an RMS developed in Chile for road networks exposed to multiple natural hazards. The RMS was implemented in a web-based geographic information system platform able to display hazard maps, quantify risk levels, prioritize mitigation strategies, and estimate direct and indirect costs and social vulnerability. The content and various capacities of the system are detailed, following the steps marked in its framework. This first version of SIGeR-RV considers seismic hazard, volcanic lahars, and hydro-meteorological hazards that affect road platforms, bridges, tunnels, and cut slopes. The system currently serves as a tool for the Ministry of Public Works of Chile and other decision makers to estimate budget requirements for increasing resilience of the road network, identifying vulnerable road segments, and assessing the socioeconomical impacts of risk reduction.

Effectiveness of seismic mitigation strategies for the Italian masonry building heritage

The seismic events that struck Italy in the last decades have highlighted the high seismic vulnerability of the built heritage. It is therefore of utmost importance to plan and prioritize mitigation strategies that can effectively reduce the amount of economic losses and the number of casualties produced by earthquakes.This work focuses on the definition of vulnerability for Italian residential masonry buildings. Fragility curves are elaborated for a database of 445 buildings through the software Vulnus 4.0, both for the as-built configuration and different intervention solutions. These vulnerability models are then combined with hazard and exposure data within the IRMA platform (Italian Risk MAps) to elaborate seismic risk maps for Italy, which show the possible losses for different return periods and time windows. By comparing the results provided by IRMA for the as-built configuration and the retrofit cases, it is possible to quantify the effectiveness of different mitigation strategies in terms of vulnerability reduction.

Prioritizing the mitigation strategies to lean and green barriers using fuzzy BWM-FTOPSIS method in the food supply chain: an empirical case study

Purpose This paper aims to assist managers and food supply chain practitioners in efficiently implementing lean and green (LG) practices for sustainability. Examining barriers to LG practices, as well as prioritizing mitigation strategies in the food supply chain, are all part of this work. Design/methodology/approach Using a combination of literature review and expert team inputs, this paper investigated the LG barriers and their solutions under PESTEL (Political, Economic, Social, Technological, Environmental and Legal) framework. To prioritize them, this work used the fuzzy best worst method (fuzzy BWM) with the fuzzy technique for order preference by similarity to ideal solution (FTOPSIS). Findings From the fuzzy BWM, the economic barriers were identified as the most significant. From the FTOPSIS approach, top management expertise and commitment to adopting LG practices were identified as the best solution for overcoming the barriers. Practical implications This paper discusses the barriers and solutions for successfully implementing LG techniques from the real-time food supply chain. The practitioners and food chain managers welcomed the methodology for its use in prioritizing the barriers to LG practices. Conclusions drawn from this work were found to be realistic. Originality/value The original contribution of this study is to present the model framework for barriers and solutions of LG practices in the dairy supply chain using the hybrid MCDM technique.

Health Risk-Oriented Source Apportionment of Hazardous Volatile Organic Compounds in Eight Canadian Cities and Implications for Prioritizing Mitigation Strategies.

Traditionally, environmental authorities make regulatory policies for controlling volatile organic compound (VOC) pollution based on the mitigation of dominant VOC sources. However, the emission from each VOC source has a unique combination of VOC species of different toxicities. Without quantitatively assessing the health risk associated with each source, the effectiveness of the mitigation policy could be undermined. To address this shortcoming, we developed a new health risk-oriented source apportionment method that can provide quantitative health risk assessment and source-specific mitigation strategies for hazardous VOCs. We estimated that the integrated inhalation cancer risk (ICR) of hazardous VOCs was 7.7 × 10-5 in Western Canada, indicating a 100% likelihood of exceeding Health Canada's acceptable risk level (1.0 × 10-5). Anthropogenic sources were responsible for 56.3-73.8% of cancer risks across eight Canadian cities except for the regional background island, where natural sources contributed over 77% to the integrated ICR. Thus, substantial environmental and health cobenefits could be achieved via reducing the ambient levels of benzene and 1,3-butadiene by 39.3-75.7 and 14-69.3%, respectively, and mitigating emissions from fuel combustion (by 31.3-54.1%), traffic source (3.0-36.8%), and other anthropogenic sources (5.3-20.1%) in Western Canada. Our study has significant implications for prioritizing air pollution mitigation policies, especially for quantitative reduction of hazardous air pollutants.

Evaluation of the Impact of Compliance with Mitigation Strategies and Frequency of Restaurant Surface Cleaning and Sanitizing on Control of Norovirus Transmission from Ill Food Employees Using an Existing Quantitative Risk Assessment Model

Reduction of foodborne illness caused by norovirus (NoV) continues to be a focus for the food safety community. Using a previously published quantitative risk assessment model, we evaluated more than 60 scenarios examining the impact of implementation of and compliance with risk management strategies identified in the U.S. Food and Drug Administration Food Code for (a) surface cleaning and sanitizing, (b) hand hygiene, (c) exclusion, or (d) restriction of ill employees. Implementation of and compliance with hand hygiene and ill food employee exclusion strategies had the largest impact on the predicted number of highly contaminated food servings and associated consumer illnesses. In scenarios in which gloves were always worn and hand washing compliance was 90%, the model estimated reductions in the number of highly contaminated food servings and ill consumers to 39 and 43% of baseline estimates (i.e., typical practice), respectively. Reductions were smaller when gloves were never worn. Hand washing compliance after using the restroom strongly impacted predicted numbers of highly contaminated servings and consumer illnesses. Ten percent compliance with removing or excluding ill food employees was predicted to increase the number of highly contaminated food servings and ill consumers to 221 and 213% of baseline estimates, respectively. Ninety-four percent compliance with exclusion of ill food employees was predicted to decrease these numbers to 69 and 71% of baseline estimates, respectively. Surface cleaning in food establishments had a relatively small impact on these measures. Restriction of food employees (removed from contact with food and food contact equipment and utensils) was not effective for reducing NoV illness unless this restriction included additional provisions. The results from this study can help risk managers prioritize mitigation strategies and their implementation for controlling the transmission of NoV and subsequent consumer foodborne illness.

Remote sensing of methane plumes: instrument tradeoff analysis for detecting and quantifying local sources at global scale

Abstract. Methane (CH4) is the second most important anthropogenic greenhouse gas with a significant impact on radiative forcing, tropospheric air quality, and stratospheric water vapor. Remote sensing observations enable the detection and quantification of local methane emissions across large geographical areas, which is a critical step for understanding local flux distributions and subsequently prioritizing mitigation strategies. Obtaining methane column concentration measurements with low noise and minimal surface interference has direct consequences for accurately determining the location and emission rates of methane sources. The quality of retrieved column enhancements depends on the choices of the instrument and retrieval parameters. Here, we studied the changes in precision error and bias as a result of different spectral resolutions, instrument optical performance, and detector exposure times by using a realistic instrument noise model. In addition, we formally analyzed the impact of spectrally complex surface albedo features on retrievals using the iterative maximum a posteriori differential optical absorption spectroscopy (IMAP-DOAS) algorithm. We built an end-to-end modeling framework that can simulate observed radiances from reflected solar irradiance through a simulated CH4 plume over several natural and artificial surfaces. Our analysis shows that complex surface features can alias into retrieved methane abundances, explaining the existence of retrieval biases in current airborne methane observations. The impact can be mitigated with higher spectral resolution and a larger polynomial degree to approximate surface albedo variations. Using a spectral resolution of 1.5 nm, an exposure time of 20 ms, and a polynomial degree of 25, a retrieval precision error below 0.007 mole m−2 or 1.0 % of total atmospheric CH4 column can be achieved for high albedo cases, while minimizing the bias due to surface interference such that the noise is uncorrelated among various surfaces. At coarser spectral resolutions, it becomes increasingly harder to separate complex surface albedo features from atmospheric absorption features. Our modeling framework provides the basis for assessing tradeoffs for future remote sensing instruments and algorithmic designs. For instance, we find that improving the spectral resolution beyond 0.2 nm would actually decrease the retrieval precision, as detector readout noise will play an increasing role. Our work contributes towards building an enhanced monitoring system that can measure CH4 concentration fields to determine methane sources accurately and efficiently at scale.

Multidimensional risk classification with global sensitivity analysis to support planning operations in a transportation network of natural gas pipelines

Multidimensional risk assessment for natural gas pipelines has proved to be an efficient approach for companies to evaluate multiple risk perspectives and to determine different mitigation actions. Therefore, making decisions to ensure the system's best performance, taking into account safety issues, is a challenging task. This paper proposes a multidimensional risk classification with global sensitivity analysis by making a structured analysis of uncertainty that uses a Monte Carlo Simulation and visualization tools. For this analysis, five scenarios of a group of parameters are set according to operational and technical issues, consequences and the preferential properties of the model for pipeline sections. Then, sensitivity levels are statistically validated and defined for each section, thereby generating information on modifications to the classification of levels of risk. For example, 10% variation in the group of parameters related to the Critical Danger Radius (CDR), 6 out of 8 pipeline sections present high sensitivity levels; showing that their original risk classifications do not remain the same. Also, 4 sections are expected to be in the high risk class, while 1 section is expected to be in medium class and 3 sections in the low risk class. Consequently, managers should prioritize mitigation strategies to those sections with significant risk categories The advantages of our proposals are twofold. First, the proposed methodology enables decisions to be made on classifying risks which incorporate evidence on the sensitivity and uncertainties of natural gas pipelines. Secondly, effective information on classifying risk is provided to the decision-maker by using statistical validations and visualization tools. Thus, managers can enhance their perception of risks with regard to a natural gas pipeline, can generate insights to control mitigations and allocate resources to ensure safety for people, and can avoid losses regarding the environment and their companies' properties.

High prevalence of vitamin D deficiency among the South Asian adults: a systematic review and meta-analysis

BackgroundVitamin-D deficiency is linked to a wide range of chronic and infectious diseases. Body of literature suggested that the prevalence of this deficiency can have geographical variation. Although vitamin D deficiency is frequently reported in the South Asian population, the scarcity of systematic reviews and meta-analysis means the true extent of the disease and the underlying factors causing it are poorly characterized.MethodsA systematic search was performed using two databases (PubMed and Scopus) and one search engine (Google Scholar) for original studies on the South Asian population (published from January 1, 2001, to December 31, 2019). Following the search, a random effect meta-analysis was performed to calculate population-level weighted average, the pooled prevalence of deficiency, and heterogeneity of vitamin D among different countries and genders; in addition to South Asia as a whole.ResultsOur study, based on our selection criteria was narrowed down to a total of 44,717 participants; which spanned over 65 studies from five South Asian countries. Overall, the pooled prevalence of deficiency was 68% [95% CI: 64 to 72%] with significant heterogeneity (I2 = 98%; p = 0.00). The average level of vitamin D ranged from 4.7 to 32 ng/mL, with a weighted mean of 19.15 ng/mL (weighted standard deviation 11.59 ng/mL). The highest prevalence of vitamin D deficiency was found in Pakistan (73%;95% CI: 63 to 83%) followed by Bangladesh (67%; 95% CI: 50 to 83%), India (67%; 95% CI: 61 to 73%), Nepal (57%; 95% CI: 53 to 60%) and Sri Lanka (48%; 95% CI: 41 to 55%), respectively. This finding indicated a high degree of heterogeneity among the population. (I2 = 98.76%), Furthermore, a gender-wise analysis suggested that in South Asia, the prevalence of vitamin D deficiency was higher in females than males.ConclusionOur findings reveal highly prevalent and variable vitamin D deficiency among the adults of different South Asian countries. Findings from this review would be helpful to generate hypotheses and explore the factors affecting the inter-country variability, alongside strengthening evidence for governments to prioritize mitigation strategies in this region.

Evaluation of CO2 Emissions Reduction Strategies in the Iranian Cement Industry

Portland cement as the main constituent of conventional concrete is the most widely used cementitious material in the construction industry. But Portland cement production has major environmental disadvantages, including high energy consumption and carbon dioxide (CO2) emissions. So that production of Portland cement is accounted for 7 to 10% of global CO2 emissions. Considering the amount of Portland cement production and CO2 emissions in Iran, it can be concluded that Iran is facing environmental problems caused by cement production. Hence, various CO2 emissions mitigation strategies of Iranian cement industry have been evaluated in this study. This research work applied Analytical Hierarchy Process (AHP) method to evaluate and prioritize mitigation strategies. The obtained results showed that among the 16 strategies studied, clinker substitution (blended cement) and production of low carbon cements such as geopolymer cement were recognized as the most important strategies to reduce CO2 emissions in the Iranian cement industry.

A text-mining based cyber-risk assessment and mitigation framework for critical analysis of online hacker forums

Online hacker communities are meeting spots for aspiring and seasoned cybercriminals where they engage in technical discussions, share exploits and relevant hacking tools to be used in launching cyber-attacks on business organizations. Sometimes, the affected organizations can detect these attacks in advance, with the help of cyber-threat intelligence derived from the explicit and implicit features of hacker communication in these forums. Herein, we proposed a novel text-mining based cyber-risk assessment and mitigation framework, which performs the following critical tasks. (i) Cyber-risk Assessment - to identify hacker expertise (i.e., newbie, beginner, intermediate, and advanced) using explicit and implicit features applying various classification algorithms. Among these features, cybersecurity keywords, sharing of attachments, and sentiments emerged as significant. Further, we found that expert hackers demonstrate leadership in the online forums that eventually serve as communities of practice. Consequently, novice hackers gradually develop their cyber-attack skills through prolonged observations, interactions, and external influences in this social learning process. (ii) Cyber-risk mitigation – computes financial impact for every {hacker expertise, attack-type} combination, and then by ranking them on a {likelihood, impact} decision-matrix to prioritize mitigation strategies in affected organizations. Through these novel recommendations, our framework can guide managers to decide on appropriate cybersecurity controls using an {expected loss, probability, attack-type, hacker expertise} metric against financial losses due to cyber-attacks.

Assessing the degree of tidal flood damage to salt harvesting landscape using synthetic approach and GIS - Case study: Cirebon, West Java

Tidal flood implications for agricultural areas have been relatively neglected in research due to their relatively low economic values. Nevertheless, many rural coastal communities are facing these types of floods regularly, especially salt harvesters. Based on a synthetic approach using damage functions and the Geographic Information System, this study applies previous simulations from a hydrodynamic model to quantify vulnerability through flood depth and duration. A depth-duration damage function using a synthetic approach was applied to develop damage curves based on previous events. Data was collected through a questionnaire aimed at salt harvesters’ group representatives. This data was used to evaluate the direct monetary loss related to two recorded events in Cirebon, West Java: in June 2016 (Event A) and in May 2018 (Event B). Those events brought losses of about Indonesian Rupiah (IDR) 74.11 × 109 (5.37%) and IDR 13.79 × 109 (8.1%) for Event A and Event B respectively. This study reveals the different vulnerability levels of the events, which represent different production stages in salt harvesting. This paper presents methods and information to support risk management in salt harvesting, including prioritizing mitigation strategies.

Breaks in MODIS time series portend vegetation change: verification using long‐term data in an arid grassland ecosystem

Frequency and severity of extreme climatic events are forecast to increase in the 21st century. Predicting how managed ecosystems may respond to climatic extremes is intensified by uncertainty associated with knowing when, where, and how long effects of extreme events will be manifest in an ecosystem. In water-limited ecosystems with high inter-annual variability in rainfall, it is important to be able to distinguish responses that result from seasonal fluctuations in rainfall from long-term directional increases or decreases in precipitation. A tool that successfully distinguishes seasonal from directional biomass responses would allow land managers to make informed decisions about prioritizing mitigation strategies, allocating human resource monitoring efforts, and mobilizing resources to withstand extreme climatic events. We leveraged long-term observations (2000-2013) of quadrat-level plant biomass at multiple locations across a semiarid landscape in southern New Mexico to verify the use of Normalized Difference Vegetation Index (NDVI) time series derived from 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) data as a proxy for changes in aboveground productivity. This period encompassed years of sustained drought (2000-2003) and record-breaking high rainfall (2006 and 2008) followed by subsequent drought years (2011 through 2013) that resulted in a restructuring of plant community composition in some locations. Our objective was to decompose vegetation patterns derived from MODIS NDVI over this period into contributions from (1) the long-term trend, (2) seasonal cycle, and (3) unexplained variance using the Breaks for Additive Season and Trend (BFAST) model. BFAST breakpoints in NDVI trend and seasonal components were verified with field-estimated biomass at 15 sites that differed in species richness, vegetation cover, and soil properties. We found that 34 of 45 breaks in NDVI trend reflected large changes in mean biomass and 16 of 19 seasonal breaks accompanied changes in the contribution to biomass by perennial and/or annual grasses. The BFAST method using satellite imagery proved useful for detecting previously reported ground-based changes in vegetation in this arid ecosystem. We demonstrate that time series analysis of NDVI data holds potential for monitoring landscape condition in arid ecosystems at the large spatial scales needed to differentiate responses to a changing climate from responses to seasonal variability in rainfall.

Exposure to volatile organic compounds and associated health risks in windsor, Canada

ObjectiveWe examined relationships among measurements of volatile organic compounds (VOCs) and performed cancer and non-cancer risk assessments to determine potential adverse health effects. MethodsPersonal, indoor and outdoor concentrations of VOCs were measured for 50 non-smoking adults in Windsor, Ontario during winter and summer 2005. The potential excess cancer cases associated with personal concentration were estimated by the Inhalation Unit Risk (USEPA IRIS) and Tumorigenic Concentration (Health Canada). Inhalation Reference Concentration (RfC) was used to evaluate non-cancer adverse effects. Indoor and personal concentrations were statistically compared. Correlations of personal concentrations were estimated to investigate the associations among VOCs. ResultsEstimated median lifetime excess cancer risks (95th percentile upper-bound) of benzene and 1,3-butadiene were 8.09 and 4.77 per 1 million. Acetaldehyde presented the highest non-cancer risk. For some VOCs, the personal and indoor geometric means were similar, but arithmetic personal means were higher than the arithmetic indoor means (p < 0.05). Negative correlations among personal VOC concentrations were not sizable. ConclusionEvaluation of potential cancer and non-cancer risks from VOCs at the personal level should not rely merely on ambient (indoor and outdoor) VOC concentrations. Our analysis showed that personal risk had a complicated pattern that needs further investigation. Our results can be used to prioritize mitigation strategies to protect human health.

Estimating Catchment Vulnerability to Diffuse Herbicide Losses from Hydrograph Statistics

The prediction of diffuse herbicide losses to surface waters using process-based models is data and time demanding. There is a need for simpler and more efficient catchment screening tools. We developed and tested a new proxy for screening catchments for their vulnerability to diffuse herbicide losses relying on widely available river flow data only. The proxy combines the fast flow index (FFI) (i.e., the long-term average proportion of fast flow to total discharge) and the fast flow volume (FFVs) (i.e., the stormflow component of the hydrograph during the spring flush period). We tested the proxy and the underlying hypotheses by regression analyses of existing high-frequency, multiannual monitoring datasets of atrazine, metolachlor, alachlor, and acetochlor concentrations from six US and European catchments. The percentages of applied amounts of three of the four herbicides lost to surface water were positively correlated with the FFV, and the slope of the correlation was positively correlated with the FFI. Based on the empirical data, we derived quantitative proxies to estimate atrazine and metolachlor losses based on the measured FFI and FFV values. The application to 65 European catchments revealed that many of them had a lower vulnerability than the six test catchments. The observed FFI dependency of the slope seems reasonable because with increasing FFI less rain is needed to trigger fast flow and more herbicide is available in the topsoil. The proposed FFI-FFVs proxy can guide researchers and authorities in selecting monitoring areas, setting monitoring results into context, and prioritizing mitigation strategies according to catchment vulnerability.