Assessment of Hungarian Consumers’ Exposure to Pesticide Residues Based on the Results of Pesticide Residue Monitoring between 2017 and 2021

The short-term intake (ESTI) of pesticide residues in Hungarian consumers was assessed based on 2331 test results obtained during the 2017–2021 monitoring program on frequently analyzed apples, sour cherries, table grapes, peaches, nectarines, peppers, and strawberries (23.5% of all samples taken from 119 crops). The age-specific consumption data were obtained from national food consumption surveys (2009 and 2018–2020). The exposure was characterized by Hazard Quotient and Hazard Index considering the acute reference doses of pesticide residues detected in the samples. When ESTI was calculated with all detected “single” residues and a variability factor of 3.6, recommended for evaluation of monitoring results, the HI only exceeded 1 for children <3 years old eating grapes (1.50–1.81). HI was <1 when any of the six foods were eaten together within one day. Between forty and fifty percent of samples contained 2–23 residues. Though the individual residue concentrations were below the corresponding MRLs, multiple residues being present in one sample resulted in maximum HI values in apples (1.14); grapes (6.57); peaches and nectarines (2.57); strawberries (2.74); and peppers (10.44). Residues with low ARfD values contributed the most. Applying HI is simple, but provides only point estimates; therefore, it should only be used in first-tier risk assessment

Evaluation of the Results of Pesticide Residue Analysis in Food Sampled between 2017 and 2021

As mandated by the EU and the national risk management duties, pesticide residues were determined by four specialized laboratories in 9924 samples taken from 119 crops of economic importance in Hungary and imported foodstuffs during 2017–2021. The screening method applied covered 622 pesticide residues as defined for enforcement purposes. The limit of detection ranged between 0.002 and 0.008 mg/kg. The 1.0% violation rate concerning all commodities was lower than in the European Union. No residue was detectable in 45.9% of the samples. For detailed analyses, six commodities (apple, cherry, grape, nectarine/peach, sweet peppers, and strawberry) were selected as they were analyzed in over 195 samples and most frequently contained residues. Besides testing their conformity with national MRLs, applying 0.3 MRL action limits for pre-export control, we found that 73% of the sampled lots would be compliant with ≥90% probability based on a second independent sampling. Multiple residues (2–23) in one sample were detected in 36–50% of the tested lots. Considering the provisions of integrated pest management, and the major pests and diseases of selected crops, normally three to four and exceptionally, seven to nine active ingredients with different modes of action should suffice for their effective and economic protection within four weeks before harvest

Quality Control of Pesticide Residue Measurements and Evaluation of Their Results

Pesticide residues are monitored in many countries around the world. The main aims of the programs are to provide data for dietary exposure assessment of consumers to pesticide residues and for verifying the compliance of the residue concentrations in food with the national or international maximum residue limits. Accurate residue data are required to reach valid conclusions in both cases. The validity of the analytical results can be achieved by the implementation of suitable quality control protocols during sampling and determination of pesticide residues. To enable the evaluation of the reliability of the results, it is not sufficient to test and report the recovery, linearity of calibration, the limit of detection/quantification, and MS detection conditions. The analysts should also pay attention to and possibly report the selection of the portion of sample material extracted and the residue components according to the purpose of the work, quality of calibration, accuracy of standard solutions, and reproducibility of the entire laboratory phase of the determination of pesticide residues. The sources of errors potentially affecting the measured residue values and the methods for controlling them are considered in this article

Development of Quality Requirements of Chemical Analytical Measurements

The development of quality requirements for the analyses of chemical contaminants is reviewed from the formation of the first association of analytical chemists in 1884. Without attempting to give complete coverage, it is shown that the elaboration of quality systems is commanded by the needs of the industry and international trade. Progress along the line of the initial inter-laboratory comparison, methods validated with collaborative tests, and development of internationally harmonized guidelines and protocols to perform complex studies aiming to improve the accuracy and reliability of the results facilitate international trade, and protect consumer health, as well as the environment. The international cooperation for limiting the replication of various (e.g., analytical, toxicological) tests is promoted by multilateral agreements that are also supported by legal obligations. Notwithstanding, the rapid development of requirements and guidance documents provides only the frame for obtaining accurate, defendable results. The production of such results is the duty of the laboratory management, analysts, and study personnel who play the decisive role and bear full responsibility for the samples analyzed

High-Resolution Mapping and Assessment of Salt-Affectedness on Arable Lands by the Combination of Ensemble Learning and Multivariate Geostatistics

Soil salinization is one of the main threats to soils worldwide, which has serious impacts on soil functions. Our objective was to map and assess salt-affectedness on arable land (0.85 km2) in Hungary, with high spatial resolution, using a combination of ensemble machine learning and multivariate geostatistics on three salt-affected soil indicators (i.e., alkalinity, electrical conductivity, and sodium adsorption ratio (n = 85 soil samples)). Ensemble modelling with five base learners (i.e., random forest, extreme gradient boosting, support vector machine, neural network, and generalized linear model) was carried out and the results showed that ensemble modelling outperformed the base learners for alkalinity and sodium adsorption ratio with R2 values of 0.43 and 0.96, respectively, while only the random forest prediction was acceptable for electrical conductivity. Multivariate geostatistics was conducted on the stochastic residuals derived from machine learning modelling, as we could reasonably assume that there is spatial interdependence between the selected salt-affected soil indicators. We used 10-fold cross-validation to check the performance of the spatial predictions and uncertainty quantifications, which provided acceptable results for each selected salt-affected soil indicator (for pH value, electrical conductivity, and sodium adsorption ratio, the root mean square error values were 0.11, 0.86, and 0.22, respectively). Our results showed that the methodology applied in this study is efficient in mapping and assessing salt-affectedness on arable lands with high spatial resolution. A probability map for sodium adsorption ratio represents sodic soils exceeding a threshold value of 13, where they are more likely to have soil structure deterioration and water infiltration problems. This map can help the land user to select the appropriate agrotechnical operation for improving soil quality and yield

High-Resolution Mapping and Assessment of Salt-Affectedness on Arable Lands by the Combination of Ensemble Learning and Multivariate Geostatistics

Soil salinization is one of the main threats to soils worldwide, which has serious impacts on soil functions. Our objective was to map and assess salt-affectedness on arable land (0.85 km2) in Hungary, with high spatial resolution, using a combination of ensemble machine learning and multivariate geostatistics on three salt-affected soil indicators (i.e., alkalinity, electrical conductivity, and sodium adsorption ratio (n = 85 soil samples)). Ensemble modelling with five base learners (i.e., random forest, extreme gradient boosting, support vector machine, neural network, and generalized linear model) was carried out and the results showed that ensemble modelling outperformed the base learners for alkalinity and sodium adsorption ratio with R2 values of 0.43 and 0.96, respectively, while only the random forest prediction was acceptable for electrical conductivity. Multivariate geostatistics was conducted on the stochastic residuals derived from machine learning modelling, as we could reasonably assume that there is spatial interdependence between the selected salt-affected soil indicators. We used 10-fold cross-validation to check the performance of the spatial predictions and uncertainty quantifications, which provided acceptable results for each selected salt-affected soil indicator (for pH value, electrical conductivity, and sodium adsorption ratio, the root mean square error values were 0.11, 0.86, and 0.22, respectively). Our results showed that the methodology applied in this study is efficient in mapping and assessing salt-affectedness on arable lands with high spatial resolution. A probability map for sodium adsorption ratio represents sodic soils exceeding a threshold value of 13, where they are more likely to have soil structure deterioration and water infiltration problems. This map can help the land user to select the appropriate agrotechnical operation for improving soil quality and yield