Farming, pesticide exposure and respiratory health: a cross-sectional study in Thailand.

OBJECTIVE: To assess the association of lung function and respiratory symptoms with farming, particularly pesticide use, in an agricultural province in Thailand. METHODS: We undertook a cross-sectional survey of adults aged 40-65 in Nan province, Thailand, between May and August 2019. We randomly recruited 345 villagers and enriched the sample with 82 government employees. All participants performed post-bronchodilator spirometry and completed a questionnaire covering information on respiratory symptoms, farming activities, pesticide use and known risk factors for respiratory disease. Associations of respiratory outcomes with farming and pesticide exposures were examined by multivariable regression analysis. RESULTS: The response rate was 94%. The prevalence of chronic airflow obstruction among villagers was 5.5%. Villagers had, on average, a lower percent predicted post-bronchodilator forced expiratory volume in one second/forced vital capacity (FEV1/FVC) than government employees (98.3% vs 100.3%; p=0.04). There was no evidence of association of lung function with farming activities, the use of specific herbicides (glyphosate and paraquat), insecticides (organophosphates and pyrethroids) or fungicides. The exceptions were poultry farming, associated with chronic cough and an increase of FEV1/FVC, and atrazine, for which duration (p-trend <0.01), intensity (p-trend <0.01) and cumulative hours (p-trend=0.01) of use were all associated with higher FEV1/FVC in an exposure-response manner. Cumulative hours (-280 mL/hour), low duration (-270 mL/year) and intensity (-270 mL/hour/year) of atrazine use were associated with lower FVC. CONCLUSIONS: Chronic airflow obstruction is uncommon among villagers of an agricultural province in Nan, Thailand. Farming and pesticide use are unlikely to be major causes of respiratory problems there.

A tiered approach to prioritizing registered pesticides for potential cancer hazard evaluations: implications for decision making.

BACKGROUND: Over 800 pesticides are registered for use in the United States. Human studies indicate concern that some pesticides currently in use in large quantities may also pose a carcinogenic hazard. Our objective is to identify candidates for future hazard evaluations among pesticides used in high volumes in the United States and also classified as potential carcinogens by U.S. Environmental Protection Agency (USEPA). We also identify data gaps where further research is needed. METHODS: We used a systematic, two-tiered review approach to prioritize pesticides. First, we identified currently registered pesticides classified by USEPA as "possible", "suggestive", or "likely" human carcinogens. Among these, we selected pesticides USEPA has listed as commonly used by volume in at least one sector (agriculture, home and garden, or industry, commercial, and/or government), and those without a published hazard evaluation in the past 5 years. Second, we searched primary literature databases for peer-reviewed human cancer studies reporting pesticide-specific data published since the last USEPA carcinogenicity evaluation for each pesticide, and created evidence maps of the number of studies meeting our criteria for each identified pesticide. No evaluation of study results or risk-of-bias assessments were conducted. RESULTS: We identified 18 pesticides meeting our selection criteria, 16 pesticides had information from human cancer studies published after their initial carcinogenicity review. Of these, eight pesticides had at least three studies for one or more cancer sites: carbaryl, dichloropropene, dimethoate, mancozeb, metolachlor, pendimethalin, permethrin, and trifluralin. A major limitation in the literature revealed a shortage of studies reporting risk estimates for individual pesticides, rather pesticides were grouped by chemical class. CONCLUSIONS: Our scoping report provides a map of the existing literature on real-world exposures and human cancer that has accumulated on pesticides classified as potential carcinogens by USEPA and used in high volumes. We also illustrate that several pesticides which are "data-rich" may warrant updated authoritative hazard evaluations. Our two-tiered approach and utilization of evidence mapping can be used to inform future decision-making to update cancer hazard evaluations.

In silico prediction of chemical acute contact toxicity on honey bees via machine learning methods.

In recent years, the decline of honey bees and the collapse of bee colonies have caught the attention of ecologists, and the use of pesticides is one of the main reasons for the decline. Therefore, ecological risk assessment of pesticides is essential and necessary. In silico tools, such as QSAR models can play an important role in predicting physicochemical and biological properties of chemicals. In this study, a total of 54 classification models were developed by combination of 6 machine learning methods along with 9 kinds of molecular fingerprints based on the experimental honey bees acute contact toxicity data (LD50) of 676 structurally diverse pesticides. The best model proposed was SVM algorithm combined with CDK extended fingerprint. The analysis of the applicability domain of the model successfully excluded some extreme molecules. Additionally, 9 structural alerts about honey bees acute contact toxicity were identified by information gain and substructure frequency analysis.

WebPalestra: hortas orgânicas urbanas e comunitárias, estratégias e cuidados

O programa de Vitória, Estratégias para implantação, definições de agricultura orgânica, urbana e afins, Noções de cultivo, tratos culturais e cuidados na aquisição do produto orgânico.

Natural product sciences: an integrative approach to the innovations of plant natural products.

The study on plant natural products not only helps us understand that their structural diversity is the inevitable result of plant species diversity, but also helps us understand certain rules and unity of the inevitable connection between the two. The diversity and complexity of chemical structures of many natural products are beyond imagination before we elucidated their structures. The question that follows is what is the biological significance of these natural products. Intrigued by the relationship between plant resources, natural products and biological functions, the Hao laboratory has taken an integrative approach that employs tools and knowledge from multi-disciplines, including natural product chemistry, chemical ecology and chemical biology, to unveil the effects of plant natural products on plant resistance to diseases, and environmental acclimations. Collaborating with cell biologists, the research has resulted in discovery of new mechanisms of cellular signaling and lead compounds.

A flow scheme for cumulative assessment of pesticides for adverse liver effects.

The European Food Safety Authority (EFSA) is developing approaches to cumulative risk assessment by assigning pesticides to cumulative assessment groups (CAGs). For assignment to CAGs, EFSA relies on common toxic effects (CTEs) on the target system. The developed flow scheme for assignment to liver CAGs sequentially assesses the consistency of the CTE, its adversity, its potential to be secondary to other toxicities, its human relevance, and the relation of the NOAEL for the CTE to the overall NOAEL. If the responses to all questions are "yes", allocation to a CAG is supported; "no" stops the process.

Pesticide-Virus Interactions in Honey Bees: Challenges and Opportunities for Understanding Drivers of Bee Declines.

Honey bees are key agricultural pollinators, but beekeepers continually suffer high annual colony losses owing to a number of environmental stressors, including inadequate nutrition, pressures from parasites and pathogens, and exposure to a wide variety of pesticides. In this review, we examine how two such stressors, pesticides and viruses, may interact in additive or synergistic ways to affect honey bee health. Despite what appears to be a straightforward comparison, there is a dearth of studies examining this issue likely owing to the complexity of such interactions. Such complexities include the wide array of pesticide chemical classes with different modes of actions, the coupling of many bee viruses with ectoparasitic Varroa mites, and the intricate social structure of honey bee colonies. Together, these issues pose a challenge to researchers examining the effects pesticide-virus interactions at both the individual and colony level.

Environmental risk for aquatic and terrestrial organisms associated with drift from pesticides used in soybean crops.

Several countries included the assessment of environmental drift contamination risk for the registration of pesticides. This practice is not yet totality effective in Brazil; however, due to the large number of pesticides in use, it is important to identify the real contamination risk during pesticide spraying. Therefore, this study determined the indices of environmental risks for exposure to drift from terrestrial applications of fungicides, herbicides, and insecticides that are used in soybean crops under Brazilian climate conditions and established buffer zones for the application of these products. Based on the three prediction drift models for soybeans in Brazil, risk indices were computed for aquatic organisms and terrestrial organisms according to the modelling procedures proposed by the POCER (Pesticide Occupation and Environmental Risk) and HAIR (Harmonized Environmental Indicators for Pesticide Risk) methodologies. In general, aquatic organisms are the most sensitive to drift contamination, being chlorothalonil, trifluralin and chlorpyrifos the ones that presented the higher risk indexes. No risk was found for earthworms; in contrast, the insecticides chlorpyrifos, spinosad and thiamethoxam presented risks to bees regardless of the nozzle (droplet size) used for the determination of the drift curve, resulting in the demand for different buffer zones.

Public Health Surveillance for the Prevention of Pesticide-Related Illness in Illinois.

OBJECTIVES: There is no pesticide related illness (PRI) surveillance program in Illinois. This study examines the quality of state-based data sources for their ability to inform public health surveillance on PRI. METHODS: We estimated the counts of PRI by probabilistic data linkage of hospital discharge, emergency department, and poison center databases from 2010 to 2015. We characterized identified PRI cases. RESULTS: We identified 3867 unique cases of PRI and 6269 asymptomatic pesticide exposures. Out of the 3867 PRI cases, there were 1319 emergency department visits and 321 hospitalizations. We identified 13 deaths and 1640 major or moderate effects from PRI. Over half of ingestion related exposures occurred in children aged 0 to 4 years. Workers' compensation and Emergency Medical Service data were unusable. CONCLUSION: An effective public health surveillance on PRI requires reliable state data sources and cost-effective methods of assembling data from multiple sources.

Differences in classification for skin corrosion/irritation in EU and Ukraine: Case study of alternative (in vitro and in silico) methods application for classification of pesticide active ingredient imazamox.

In Ukraine Globally Harmonized System of classification of chemicals has not been implemented yet. In this article we analyze differences between GHS/CLP classification systems and Hygienic Classification of Pesticides by the Degree of Hazard currently in force in Ukraine in respect of approach and criteria for classification of effects on skin. As a case study, we conducted in silico modelling of herbicide imazamox using skin irritation/corrosion modules of ToxTree. The prediction of ToxTree was "Not Corrosive to skin". Then skin irritation and skin corrosion in vitro tests (OECD TGs 439, 431) were conducted. Classification of this substance based on in vitro and in vivo results according to GHS/CLP was the same, while it was not possible based on in vitro results to assign certain hazard class of Ukrainian classification due to difference in its and GHS/CLP criteria. However, ongoing process of harmonization of Ukrainian legislation with EU will give opportunity not only use alternative methods, but also adopt most recent advances and incorporate data from non-animal methods directly into classification criteria.

Why pesticides with mutagenic, carcinogenic and reproductive risks are registered in Brazil.

Brazil is the biggest market for pesticides in the world. In the registration process, a pesticide must be authorized by the Institute of the Environment, Health Surveillance Agency and Ministry of Agriculture. Evaluations follow a package of toxicological studies submitted by the companies and also based on the Brazilian law regarding pesticides. We confronted data produced by private laboratories, submitted to the Institute of the Environment for registration, with data obtained from scientific databases, corresponding to mutagenicity, carcinogenicity and teratogenicity of pesticides. All studies submitted by the companies were carried out by private laboratories. From 247 pesticide formulations analyzed, none showed positive results for mutagenicity, carcinogenicity or teratogenicity. From 574 articles in the scientific literature, 84% published by public laboratories showed positive results, while 79% of those showing negative results came from private laboratories. There is an ethical concern about a conflict of interest between public/independent laboratories and private laboratories that produce data for registering pesticides. We demonstrated that there is a clear contradiction between public and private laboratories. Brazilian regulatory authorities have approved the registration of pesticides based almost exclusively on the monographs provided by the pesticide industry, because the use of scientific articles or information from the independent literature is strongly belittled by the industry. Pesticide companies argue that scientific articles cannot be trusted. Also, according to the industry, pesticide registration cannot be refused based on results from scientific articles. Thus, the registration of pesticides with mutagenic, carcinogenic and teratogenic risks has been approved in Brazil.

Comparative cytotoxicity of plant protection products and their active ingredients.

Toxicological testing of plant protection products (PPPs) is a legal requirement in the EU. The whole PPP formulation is tested for acute endpoints in vivo during approval procedure of PPPs. However, alternative methods such as the CLP calculation method (CM) are employed increasingly. In the first part of this study we analysed PPPs for the correlation of GHS classifications resulting mainly from in vivo LD50-values with classifications obtained from calculated LD50-values using the CM. Accordingly, the CM predicted 80% of the PPPs correctly. However 31% of classified products were not identified revealing a considerable inaccuracy of this method. Based on these results ten PPPs and corresponding ASs were further tested in a cytotoxicity assay employing 3T3 and hFF cells (one PPP and corresponding AS were tested in HepaRG cells). The study outcome revealed that the cytotoxicity data did not reliably reflect differences in toxicity between ASs and PPPs. Especially organic solvent based formulations demonstrated a higher cytotoxicity than water based formulations independently to their toxicity in vivo. Overall, the cytotoxicity test did not provide a more robust method than the CM. However, the database consisting of ten PPPs was small and therefore no robust conclusions can be drawn.

Subjective Symptoms of Male Workers Linked to Occupational Pesticide Exposure on Coffee Plantations in the Jarabacoa Region, Dominican Republic.

Acute and sub-acute effects of pesticide use in coffee farmers have rarely been investigated. In the present field study, self-reported health symptoms from 38 male pesticide users were compared to those of 33 organic farmers. Results of cytological findings have been reported in an accompanying paper in this issue. The present second part of the study comprises a questionnaire based survey for various, potentially pesticide related symptoms among the coffee farmers. Symptom rates were generally higher in exposed workers, reaching significance in nine out of 19 assessed symptoms. Significantly increased symptom frequencies were related to neurotoxicity, parasympathic effects and acetylcholine esterase inhibition, with the highest differences found for excessive salivation, dizziness and stomach ache. We revealed a lack of precautionary measures in the majority of farmers. Better education, regulations, and safety equipment are urgently needed.

A colorimetric sensor array based on sulfuric acid assisted KMnO4 fading for the detection and identification of pesticides.

Pesticides play a critical role in improving crop yield in modern agriculture, but their residues significantly harm the environment and human health. Herein, a novel and simple colorimetric sensor array built on sulfuric acid assisted KMnO4 fading strategy has been developed for pesticides detection and discrimination. This sensor array is facilely fabricated by KMnO4 and sulfuric acid through simply adjusting their concentrations and ratios. Hierarchical clustering analysis (HCA) demonstrates that the as-fabricated colorimetric sensor array has a high dimensionality, and shows excellent capability to recognize common kinds of pesticides from potential interferants. Semi-quantitative detection was achieved through combining HCA and corresponding fitting curves. Moreover, the proposed sensor array was successfully applied to detect pesticide residues (e.g. carbaryl) in real samples. The strategy described herein will not only "maximally" simplify the design and fabrication approach, but expand the application fields of colorimetric sensor array methodology towards weak-reactive analytes.

QSAR modeling in ecotoxicological risk assessment: application to the prediction of acute contact toxicity of pesticides on bees (Apis mellifera L.).

Despite their indisputable importance around the world, the pesticides can be dangerous for a range of species of ecological importance such as honeybees (Apis mellifera L.). Thus, a particular attention should be paid to their protection, not only for their ecological importance by contributing to the maintenance of wild plant diversity, but also for their economic value as honey producers and crop-pollinating agents. For all these reasons, the environmental protection requires the resort of risk assessment of pesticides. The goal of this work was therefore to develop a validated QSAR model to predict contact acute toxicity (LD50) of 111 pesticides to bees because the QSAR models devoted to this species are very scarce. The analysis of the statistical parameters of this model and those published in the literature shows that our model is more efficient. The QSAR model was assessed according to the OECD principles for the validation of QSAR models. The calculated values for the internal and external validation statistic parameters (Q 2 and [Formula: see text] are greater than 0.85. In addition to this validation, a mathematical equation derived from the ANN model was used to predict the LD50 of 20 other pesticides. A good correlation between predicted and experimental values was found (R 2 = 0.97 and RMSE = 0.14). As a result, this equation could be a means of predicting the toxicity of new pesticides.

Clustering pesticides according to their molecular properties, fate, and effects by considering additional ecotoxicological parameters in the TyPol method.

Understanding the fate and ecotoxicological effects of pesticides largely depends on their molecular properties. We recently developed "TyPol" (Typology of Pollutants), a classification method of organic compounds based on statistical analyses. It combines several environmental (sorption coefficient, degradation half-life) and one ecotoxicological (bioconcentration factor) parameters, to structural molecular descriptors (number of atoms in the molecule, molecular surface, dipole moment, energy of orbitals, etc.). The present study attempts to extend TyPol to the ecotoxicological effects of pesticides on non-target organisms, based on data analysis from available literature and databases. It revealed that relevant ecotoxicological endpoints for terrestrial organisms (e.g., soil microorganisms, invertebrates) that support a range of ecosystemic services are lacking as compared to aquatic organisms. The availability of ecotoxicological parameters was also lower for chronic than for acute ecotoxicity endpoints. Consequently, seven parameters were included for acute (EC50, LC50) and chronic (NOEC) ecotoxicological effects for one terrestrial (Eisenia sp.) and three aquatic (Daphnia sp., algae, Lemna sp.) organisms. In this new configuration, we used TyPol to classify 50 pesticides into different clusters that gather molecules with similar environmental behaviors and ecotoxicological effects. The classification results evidenced relationships between molecular descriptors, environmental parameters, and the added ecotoxicological endpoints. This proof-of-concept study also showed that TyPol in silico classification can successfully address new scientific questions and be expanded with other parameters of interest.

Prediction of pesticide acute toxicity using two-dimensional chemical descriptors and target species classification.

Previous modelling of the median lethal dose (oral rat LD50) has indicated that local class-based models yield better correlations than global models. We evaluated the hypothesis that dividing the dataset by pesticidal mechanisms would improve prediction accuracy. A linear discriminant analysis (LDA) based-approach was utilized to assign indicators such as the pesticide target species, mode of action, or target species - mode of action combination. LDA models were able to predict these indicators with about 87% accuracy. Toxicity is predicted utilizing the QSAR model fit to chemicals with that indicator. Toxicity was also predicted using a global hierarchical clustering (HC) approach which divides data set into clusters based on molecular similarity. At a comparable prediction coverage (~94%), the global HC method yielded slightly higher prediction accuracy (r2 = 0.50) than the LDA method (r2 ~ 0.47). A single model fit to the entire training set yielded the poorest results (r2 = 0.38), indicating that there is an advantage to clustering the dataset to predict acute toxicity. Finally, this study shows that whilst dividing the training set into subsets (i.e. clusters) improves prediction accuracy, it may not matter which method (expert based or purely machine learning) is used to divide the dataset into subsets.

Risk-based prioritization method for the classification of groundwater pesticide pollution from agricultural regions.

Agricultural regions are a significant source of groundwater pesticide pollution. To ensure that agricultural regions with a significantly high risk of groundwater pesticide contamination are properly managed, a risk-based ranking method related to groundwater pesticide contamination is needed. In the present paper, a risk-based prioritization method for the classification of groundwater pesticide pollution from agricultural regions was established. The method encompasses 3 phases, including indicator selection, characterization, and classification. In the risk ranking index system employed here, 17 indicators involving the physicochemical properties, environmental behavior characteristics, pesticide application methods, and inherent vulnerability of groundwater in the agricultural region were selected. The boundary of each indicator was determined using K-means cluster analysis based on a survey of a typical agricultural region and the physical and chemical properties of 300 typical pesticides. The total risk characterization was calculated by multiplying the risk value of each indicator, which could effectively avoid the subjectivity of index weight calculation and identify the main factors associated with the risk. The results indicated that the risk for groundwater pesticide contamination from agriculture in a region could be ranked into 4 classes from low to high risk. This method was applied to an agricultural region in Jiangsu Province, China, and it showed that this region had a relatively high risk for groundwater contamination from pesticides, and that the pesticide application method was the primary factor contributing to the relatively high risk. The risk ranking method was determined to be feasible, valid, and able to provide reference data related to the risk management of groundwater pesticide pollution from agricultural regions. Integr Environ Assess Manag 2017;13:1052-1059. © 2017 SETAC.

Evaluation of alternative environmentally friendly matrix solid phase dispersion solid supports for the simultaneous extraction of 15 pesticides of different chemical classes from drinking water treatment sludge.

This study describes the development, optimization and validation of a method for the extraction of 15 pesticides of different chemical classes in drinking water treatment sludge (DWTS) by vortex-assisted Matrix Solid Phase Dispersion (MSPD) with determination by gas chromatography coupled to mass spectrometry. It focused on the application of alternative and different solid supports to the extraction step of the MSPD. The main parameters that influenced the extraction were studied in order to obtain better recovery responses. Recoveries ranged from 70 to 120% with RSD below 20% for all analytes. Limits of quantification (LOQ) of the method ranged from 5 to 500 µg kg-1 whereas the analytical curves showed correlation coefficients above 0.997. The method under investigation used low volume of solvent (5 mL), low sample mass (1.5 g) and low mass of chitin (0.5 g), an environmentally friendly support. It has advantages, such as speed, simplicity and low cost material, over other methods. When the method was applied, 4 out of 15 pesticides were detected in the DWTS samples in concentrations below the LOQ.

Identification and Determination of 492 Contaminants of Different Classes in Food and Feed by High-Resolution Mass Spectrometry Using the Standard Addition Method.

The methodology for the identification and determination of 492 different toxicants from a single sample in food and feed by high-resolution quadrupole time-of-flight MS in combination with HPLC has been presented. The sample preparation is easy. The analytes are pesticides of different classes, including mycotoxins, veterinary drugs, and synthetic dyes. A scheme for the identification and determination of toxicants using the standard addition method has been given. The RSDs of the analytical results presented are ≤0.15, with a 40-60 min sample screening time and a 2-3 h for the determination of the detected toxicants.