In this study, a multi-level strategy to prioritize pesticides for use in agriculture is proposed in support of protecting human health. The strategy includes four levels (production, application, ...distribution, and bioaccumulation) of screening approaches, for which the life cycle impact assessment (LCIA) and human health risk assessment (HHRA) models were applied to generate priority lists at each level. The LCIA model was used at the production level (i.e., chemical design; before pesticides being registered and entering the market), providing the inventory management based on environmental persistence and toxicity potential of pesticides, whereas the HHRA models were used at the other three levels, assessing human health risks based on pesticide emission to cropland. Priority scores (PS) of 319 pesticides were simulated with high scores indicating high priority for use in agriculture (relatively high human safety). The simulated results via the LCIA at the production level had strong positive correlations with those via the HHRA at the other three levels, indicating that the LCIA tool was capable of an initial screening of pesticides for use in agriculture. The simulated PS values of 319 pesticides indicated that some pesticides (e.g., chlorpyrifos and fipronil) with low PS values (e.g., < 8) that are currently used in some countries should be further evaluated. For other pesticides with high simulated PS values (e.g., > 15) for use in agriculture, their ecotoxicity impacts and ecological risks should be considered in protecting human health.
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•Multiple-level strategy prioritized pesticides for use in agriculture.•Priority list of 319 pesticides generated for four levels.•Priority scores derived from human health impact/risk assessment.•Pesticides with high priority scores encouraged for use in agriculture.
Pesticide residues in nectar and pollen of plants can damage honeybees; however, few modeling approaches have simulated residue levels in nectar and pollen in support of exposure assessment for ...honeybees. This study introduced a generic modeling approach based on plant uptake models and simple partitioning rules that specifies soil incorporation and foliar spray application scenarios of pesticides and is flexible for conducting variability analysis for various environmental conditions, pesticide application patterns, chemical individuals, and plant varieties. The results indicated that, in general, systemic or moderate lipophilicity (log KOW of ~2.5) pesticides have relatively high simulated residue levels in nectar and pollen because of the enhanced residue uptake process from soil. For non-systemic or highly lipophilic pesticides, the residue uptake via leaf surface deposition pathway can be enhanced, and more residues will be bioaccumulated in pollen than nectar due to a relatively high lipid content of pollen (as compared to nectar), but the overall residue levels in nectar and pollen are lower than systemic or moderately lipophilic pesticides. The variability analysis showed that environmental conditions, pesticide application patterns, chemical properties, and plant varieties cause considerable variations in simulated residue levels in nectar and pollen, indicating that spatiotemporal, chemical, and plant-related factors must be considered in pesticide exposure assessment for honeybees. Moreover, the comparison between the simulated and measured data showed a high degree of consistency, indicating that the proposed model could be used to conduct a screening-level pesticide exposure assessment for honeybees.
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•A modeling method is proposed to simulate residue levels in nectar and pollen.•Pesticides with moderate lipophilicity have high simulated residue unit doses (RUDs).•Soil incorporation promotes uptake of systemic pesticides and translocation to nectar.•Foliar application promotes uptake of non-systemic pesticides and translocation to pollen.•High spatial variability is observed in simulated RUDs of pesticides.
Pesticide residues in groundwater, mainly transported from contaminated soil, may threaten drinking water sources and cause adverse health effects. Therefore, pesticide groundwater standards were ...implemented by international environmental agencies to ensure the quality of groundwater, which serves as the direct drinking water source in many countries. However, regulatory inconsistencies are always found among groundwater, soil, drinking water, and even health standards due to the lack of communication among the regulatory processes. This study first developed a health-based regulatory chain framework to analyze pesticide groundwater regulations integrating soil, drinking water, and health regulations. Six regulatory indexes associated with probabilistic risk assessments and pesticide transport modeling were constructed to evaluate the performance of pesticide groundwater regulations identified from 56 countries. Worldwide pesticide groundwater regulations were analyzed by quantifying the impact on the downstream (exposure pathways in general) pesticide drinking water standards and human health and the influence from upstream (environmental pathways in general) soil regulations. The results indicated that in general, worldwide pesticide soil regulations do not encompass a sufficient number of pesticides or provide appropriate standard values to be compatible with groundwater regulations. The computed indexes between pesticide groundwater and drinking water regulations indicated more positive results than soil regulations because most European nations have groundwater regulations that are compatible with those of drinking water. However, most pesticide groundwater regulations could not protect human health according to the health-based indexes. Hopefully, the regulatory framework developed in this study will help environmental agencies comprehensively evaluate and establish pesticide groundwater regulations.
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•A regulatory chain framework was developed to analyze groundwater regulations.•Three-dimension regulatory indexes were constructed integrating soil, water, and health standards.•Set theory integrated potential infinity was implemented for the EU-related regulations.•Worldwide pesticide groundwater standards are threatened by the upstream soil RGVs.•Worldwide pesticide groundwater standards cannot protect the downstream human health.
Guttation consumption is a potential pathway of pesticide residue exposure in honeybees. However, modeling tools for assessing honeybee exposure to pesticide residues in guttation drops are lacking. ...In this study, we propose an indicator-based approach for qualitatively or quantitatively analyzing the guttation-based exposure pathway, allowing us to conduct region-specific pesticide residue exposure assessments for honeybees. Exposure scores (the product of guttation production and residue level scores) were established to compare or rank honeybee exposure to pesticide residues via guttation intake across locations using three specified indicators (i.e., air temperature, relative humidity, and precipitation intensity). Warm, dry regions had high residue level scores (indicating high residue levels in guttation), whereas cold, wet regions had high guttation production scores (indicating high possibilities of guttation formation on leaf surfaces); their exposure scores were a combination of these two values. We evaluated and ranked honeybee exposure to imidacloprid residue across regions in Brazil, China, the United States, and selected European Union member states, revealing that pesticide application in many Brazilian federative units may raise honeybee risks due to high exposure scores. We also compared the guttation pathway to other common exposure pathways (nectar and pollen), suggesting that for some moderately lipophilic compounds, the guttation exposure pathway may not be ignored and should be further evaluated.
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•An indicator-based approach to assessing honeybee pesticide exposure is presented.•Residue level and guttation production scores aid in determining region-specific exposure.•Guttation-based exposure pathway is generally minor compared to other exposure pathways.•The effects of moderately lipophilic pesticides on honeybees should be studied further.
To help countries worldwide regulate agricultural soil standards for organic contaminants, this study developed the pastoral-based chemical lifecycle management (PBCLM) modeling framework, which ...comprehensively models the bottom-up causation of the chemicals’ lifecycle at each level of the cattle industry and delivers top-down regulatory strategies. The lifecycle models for a total of 308 hydrophobic organic contaminants were constructed. The results indicated that the octanol-water partitioning coefficient (log KOW) values had the greater impact on the unit-legal-limit-based concentrations for contaminants at the producer level (i.e., grass) or higher. In addition, the analysis of the weather variables indicated that pastoral farming in warmer and drier places might lead to the bioaccumulation of more contaminants. By comparing the reference legal limits that were derived by the PBCLM, current soil standards might not be effective in protecting human health or harmonizing downstream food regulations. The PBCLM can help regulatory agencies better promulgate soil regulations to ensure sustainable agriculture.
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•A top-down causal modeling framework was introduced for pastoral farming.•Lifecycle models were developed across multiple levels of the cattle industry.•Regionalized health risks were evaluated for major cattle markets.•Unit legal factors were derived for 308 hydrophobic organic contaminants.•Current agricultural soil standards are not conservative enough.
Peas are among the most popular leguminous plants, consumed by both humans and animals in large quantities. Pesticides are widely used globally to increase pea yield, and as a result, pesticide ...residues can be taken up by pea plants and bioaccumulate in their fruits, including peas and pods. However, there is a lack of modeling approaches available to predict residue concentrations in peas. To address this issue, a pod fruit model (specifically designed for neutral organic compounds) was proposed to simulate the bioaccumulation process of pesticide residues in pea plants, which was developed by modifying a peel-like uptake model. The simulation results, based on green pea as the modeling demonstration, reveal that moderately-lipophilic pesticides (i.e., log KOW around 3) have higher simulated concentrations in peas at harvest compared to hydrophilic (i.e., log KOW less than 0) or highly-lipophilic (i.e., log KOW over 5) pesticides, which is due to the enhanced uptake process of moderately-lipophilic compounds in the pod-pea system, such as their ability to penetrate the pod cuticle and be transported via phloem sap. The sensitivity test and variability analysis conducted in this study revealed that the degradation kinetics, including metabolism, hydrolysis, and photolysis, had a significant impact on moderately-lipophilic pesticides due to their high simulated concentrations in the pea plant. This can result in substantial loss of residue mass via degradation. The validation of the model demonstrated that the simulation results, specifically residue concentrations in the fruit, were consistent with the harvested data. However, some inconsistency was observed immediately after pesticide application, which could be attributed to plant growth dynamics and initial surface mass distributions. The proposed pod fruit model provides new insights into the bioaccumulation process of pesticide residues in pea plants and enables high-throughput simulations of residue concentrations at harvest. To enhance the performance of the pod fruit model, future research should consider plant growth dynamics, plant uptake of ionizable compounds, and initial mass distribution functions.
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•A pod fruit model offers new insights into pesticide bioaccumulation in pea plants.•The degradation kinetics are shown to play a vital role in the simulation results.•The model could be useful for high-throughput pesticide screening in pea plants.
As pesticides can be degraded to toxic metabolites in the soil, metabolite toxicity should be considered in human health risk assessments. In this study, a screening-level modeling framework was ...developed to manage pesticides in surface soil, which was discussed under discrete and continuous emission scenarios. In addition, we selected glyphosate and its major metabolite (aminomethylphosphonic acid or AMPA) as examples to conduct screening-level risk management at regional, national, and global scales. The results indicated that if soil AMPA were not considered, human health risks could be significantly underestimated because of the large half-life of AMPA in the soil. For example, the added concentration factors of AMPA were simulated as 0.19 and 6.72 considering all major elimination pathways and considering the degradation pathway alone, respectively, indicating that AMPA formation could lead to severe extra health burdens. Furthermore, the evaluation of current glyphosate soil standards suggested that toxic metabolites should be considered in the regulatory process; otherwise, many standards could theoretically trigger high levels of soil AMPA, which could result in serious human health damage. Our proposed screening-level model can help to improve risk assessment and regulatory management of pesticides in surface soils.
•Degradation metabolites increase the human health risk of the parent compound.•AMPA can be equivalent to over six times the soil glyphosate concentrations.•Current glyphosate soil standards may not be effective to protect human health.•The proposed model can be flexibly incorporated into USEtox.
Exploring better models for evaluating the distribution of pesticide residues in soil and sediment is necessary to assess and avoid population health risk. Frequentist philosophy and probability are ...widely used in many studies to apply a log-normal distribution associated with the maximum likelihood estimation, which assumes fixed parameters and relies on a large sample size for long-run frequency. However, frequentist probability might not be suitable for analyzing pesticide residue distribution, whose parameters are affected by many complex factors and should be treated as unfixed. This study aimed to implement a Bayesian generalized log-normal (GLN) model to better understand the distribution of pesticide residues in soil and quantify population risks. The Bayesian GLN model, including location, scale, and shape parameters, was applied for the first time to dynamically evaluate pesticide residue distribution in soil and sediments. In addition, a comprehensive human health risk assessment of exposure to lindane via soil was conducted using the lifetime cancer risk for carcinogenic effect, margin of exposure for non-carcinogenic effect, and disability-adjusted life year for health damage. The Bayesian posterior analysis results indicated that the distribution of the concentration of some pesticide was better fitted to a log-Laplace (e.g., the mode value of shape parameter for lindane is 1.079) or showed mixtures of distributions within the family of log-normal distributions (e.g., the mode value of shape parameter for p,p′-DDE is 2.395), which can better explain the long-tail phenomenon of pesticide residue distribution and dynamically evaluate distribution models. For lindane, the 95% uncertainty bounds on the 95th percentile computed from 95% highest probability density regions (credible intervals) of three parameters by using the Bayesian p-box method were 2.063, 1558.609 ng/g, which is several orders of magnitude larger than the computed frequentist 95% confidence interval of 4.690, 8.095 ng/g and indicates that the population could have cancer risk concerns. These uncertainty analysis results from the Bayesian GLN approach indicated a larger variation of Lindane soil residues, which might reflect the complex and unpredictable mechanism of pesticide residue distribution including both unfixed models and distribution parameters. In summary, Bayesian GLN model is more flexible for the dynamic evaluation of pesticide soil residue distribution, retains posteriors for future data analysis, and could better quantify the uncertainties in population health risks. Therefore, this study can provide a novel and dynamical perspective of pesticide residue distribution and help better quantify health risks.
•Objective Bayesian method was implemented for dynamic evaluation of pesticide soil residue.•A shape parameter was introduced to allow flexibility of the model.•Bayesian GLN p-box better explored pesticide residue distribution uncertainties.•Population risks including cancer, non-cancer, and health damage were quantified.
The coronavirus disease 2019 (COVID-19) outbreak is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Angiotensin-converting enzyme 2 (ACE2) was rapidly identified as the ...critical functional receptor for SARS-CoV-2. ACE2 is well-known as a counter-regulator of the renin-angiotensin system (RAS) and plays a key role in the cardiovascular system. Given that ACE2 functions as both a SARS-CoV-2 receptor and a RAS modulator, the treatment for COVID-19 presents a dilemma of how to limit virus entry but protect ACE2 physiological functions. Thus, an in-depth summary of the recent progress of ACE2 research and its relationship to the virus is urgently needed to provide possible solution to the dilemma. Here, we summarize the complexity and interplay between the coronavirus, ACE2 and RAS (including anti-RAS drugs). We propose five novel working modes for functional receptor for SARS-CoV-2 infection and the routes of ACE2-mediated virus entering host cells, as well as its regulatory mechanism. For the controversy of anti-RAS drugs application, we also give theoretical analysis and discussed for drug application. These will contribute to a deeper understanding of the complex mechanisms of underlying the relationship between the virus and ACE2, and provide guidance for virus intervention strategies.
This review summarizes the complexity and interplay between the coronavirus, ACE2 and RAS (including anti-RAS drugs). Display omitted
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