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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.

The inappropriate application of pesticides to vegetable crops often results in environmental pollution, which seriously impacts the environment and human health. Given that current methods of pesticide residue detection are associated with issues such as low accuracy, high equipment cost, and complex flow, this study puts forward a new method for detecting pesticide residues on lettuce leaves. To establish this method, spectral analysis was used to determine the characteristic wavelength of pesticide residues (709 nm), machine vision equipment was improved, and a bandpass filter and light source of characteristic wavelength were installed to acquire leaf image information. Next, image preprocessing and feature information extraction were automatically implemented through programming. Several links were established for the training model so that the required feature information could be automatically extracted after the batch input of images. The pesticide residue detected using the chemical method was taken as the output and modeled, together with the input image information, using the convolutional neural network (CNN) algorithm. Furthermore, a prediction program was rewritten to generalize the input images during the prediction process and directly obtain the output pesticide residue. The experimental results revealed that when the detection device and method designed in this study were used to detect pesticide residues on lettuce leaves in a key state laboratory, the coefficient of determination of the equation reached 0.883, and the root mean square error (RMSE) was 0.134 mg/L, indicating high accuracy and that the proposed method integrated the advantages of spectrum detection and deep learning. According to comparison testing, the proposed method can meet Chinese national standards in terms of accuracy. Moreover, the improved machine vision equipment was less expensive, thus providing powerful support for the application and popularization of the proposed method.

Crop residue burning is the major biomass burning activity in China, strongly influencing the regional air quality and climate. As the cultivation pattern in China is rather scattered and intricate, it is a challenge to derive an accurate emission inventory for crop residue burning. In this study, we proposed a remote sensing-based method to estimate nitrogen oxide (NOx) emissions related to crop residue burning at the field level over Hubei, China. The new method considers differences in emission factors and the spatial distribution for different crop types. Fire radiative power (FRP) derived from moderate-resolution imaging spectroradiometer (MODIS) was used to quantify NOx emissions related to agricultural biomass combustion. The spatial distribution of different crops classified by multisource remote sensing data was used as an a priori constraint. We derived a new NOx emission database for Hubei from 2014 to 2016 with spatial resolution of 1 × 1 km. Significant seasonal patterns were observed from the NOx emission database. Peak NOx emission occurring in October was related to the residue burning in late autumn harvesting. Another peak was observed between January and April, which was due to the frequent burning of stubble before spring sowing. Our results were validated by comparing our emission inventory with geostationary satellite observations, previous studies, global fire emission database (GFED), NO2 vertical column densities (VCDs) from ozone monitoring instrument (OMI) satellite observations, and measurements from environmental monitoring stations. The comparisons showed NOx emission from GFED database was 47% lower than ours, while the evaluations from most of the statistical studies were significantly higher than our results. The discrepancies were likely related to the differences of methodology and data sources. The spatiotemporal variations of NOx emission in this study showed strong correlations with NO2 VCDs, which agreed well with geostationary satellite observations. A reasonable correlation between in situ NO2 observations and our results in agricultural regions demonstrated that our method is reliable. We believe that the new NOx emission database for crop residue burning derived in this study can potentially improve the understanding of pollution sources and can provide additional information for the design of pollution control measures.

Given their geographical proximity but differences in cultural and religious dietary customs, we hypothesize that children from the three main ethnic populations (Han, Hui, and Tibetan) residing in the Qinghai-Tibetan Plateau region differs in their non-iatrogenic antibiotic loads. To determine the antibiotic burden of the school children unrelated to medical treatment, we quantified the antibiotic residues in morning urine samples from 92 Han, 72 Tibetan, and 85 Muslim Hui primary school children aged 8 to 12 years using high-performance liquid chromatography-tandem mass spectrometry, and performed correlation analysis between these data and concurrent dietary nutrition assessments. Sixteen of the 18 targeted antibiotics (4 macrolides, 3 β-lactams, 2 tetracyclines, 4 quinolones, 3 sulfonamides, and 2 aminoanols) were identified in the urine samples with an overall detection frequency of 58.63%. The detection frequency of the six antibiotic classes ranged from 1.61% to 32.53% with ofloxacin showing the single highest frequency (18.47%). Paired comparison analysis revealed significant differences in antibiotic distribution frequency among groups, with Tibetans having higher enrofloxacin (P = 0.015) and oxytetracycline (P = 0.021) than Han children. Norfloxacin (a human/veterinary antibiotic) was significantly higher in the Hui children than in the Han children (P = 0.024). Dietary nutrient intake assessments were comparable among participants, showing adequate levels of essential vitamins and minerals across all three ethnic groups. However, significant differences in specific foods were observed among groups, notably in lower fat consumption in the Hui group. The introduction and accumulation of antibiotic residues in school children through non-iatrogenic routes (food or environmental sources) poses a serious potential health risk and merits closer scrutiny to determine the sources. While the exact sources of misused or overused antibiotics remains unclear, further study can potentially correlate ethnicity-specific dietary practices with the sources of contamination.

Accurate analytical results with known uncertainty are required for the safety assessment of pesticides and testing the conformity of marketed food and feed with the maximum residue limits. The available information on various sources of errors was examined with special emphasis to those which may remain unaccounted for based on the current practice of many laboratories. The method validation typically covers the steps of the pesticide residue determination from the extraction of spiked samples to the instrumental determination, which contribute to only 10–40% of total variance of results. Though the variability of sampling, sample size reduction and sample processing may amount to the 60–90% of total variance, it generally remains unnoticed leading to wrong decisions. Another important source of gross error is the mismatch of the residues analysed and those included in the relevant residue definition. Procedures which may be applied for eliminating or reducing the errors are discussed.