Changing climate, shifting mycotoxins: A comprehensive review of climate change impact on mycotoxin contamination.

Climate change (CC) is a complex phenomenon that has the potential to significantly alter marine, terrestrial, and freshwater ecosystems worldwide. Global warming of 2°C is expected to be exceeded during the 21st century, and the frequency of extreme weather events, including floods, storms, droughts, extreme temperatures, and wildfires, has intensified globally over recent decades, differently affecting areas of the world. How CC may impact multiple food safety hazards is increasingly evident, with mycotoxin contamination in particular gaining in prominence. Research focusing on CC effects on mycotoxin contamination in edible crops has developed considerably throughout the years. Therefore, we conducted a comprehensive literature search to collect available studies in the scientific literature published between 2000 and 2023. The selected papers highlighted how warmer temperatures are enabling the migration, introduction, and mounting abundance of thermophilic and thermotolerant fungal species, including those producing mycotoxins. Certain mycotoxigenic fungal species, such as Aspergillus flavus and Fusarium graminearum, are expected to readily acclimatize to new conditions and could become more aggressive pathogens. Furthermore, abiotic stress factors resulting from CC are expected to weaken the resistance of host crops, rendering them more vulnerable to fungal disease outbreaks. Changed interactions of mycotoxigenic fungi are likewise expected, with the effect of influencing the prevalence and co-occurrence of mycotoxins in the future. Looking ahead, future research should focus on improving predictive modeling, expanding research into different pathosystems, and facilitating the application of effective strategies to mitigate the impact of CC.

Improving plant disease classification by adaptive minimal ensembling.

A novel method for improving plant disease classification, a challenging and time-consuming process, is proposed. First, using as baseline EfficientNet, a recent and advanced family of architectures having an excellent accuracy/complexity trade-off, we have introduced, devised, and applied refined techniques based on transfer learning, regularization, stratification, weighted metrics, and advanced optimizers in order to achieve improved performance. Then, we go further by introducing adaptive minimal ensembling, which is a unique input to the knowledge base of the proposed solution. This represents a leap forward since it allows improving the accuracy with limited complexity using only two EfficientNet-b0 weak models, performing ensembling on feature vectors by a trainable layer instead of classic aggregation on outputs. To the best of our knowledge, such an approach to ensembling has never been used before in literature. Our method was tested on PlantVillage, a public reference dataset used for benchmarking models' performances for crop disease diagnostic, considering both its original and augmented versions. We noticeably improved the state of the art by achieving 100% accuracy in both the original and augmented datasets. Results were obtained using PyTorch to train, test, and validate the models; reproducibility is granted by providing exhaustive details, including hyperparameters used in the experimentation. A Web interface is also made publicly available to test the proposed methods.

UAV-Based Hyperspectral Monitoring Using Push-Broom and Snapshot Sensors: A Multisite Assessment for Precision Viticulture Applications.

Hyperspectral aerial imagery is becoming increasingly available due to both technology evolution and a somewhat affordable price tag. However, selecting a proper UAV + hyperspectral sensor combo to use in specific contexts is still challenging and lacks proper documental support. While selecting an UAV is more straightforward as it mostly relates with sensor compatibility, autonomy, reliability and cost, a hyperspectral sensor has much more to be considered. This note provides an assessment of two hyperspectral sensors (push-broom and snapshot) regarding practicality and suitability, within a precision viticulture context. The aim is to provide researchers, agronomists, winegrowers and UAV pilots with dependable data collection protocols and methods, enabling them to achieve faster processing techniques and helping to integrate multiple data sources. Furthermore, both the benefits and drawbacks of using each technology within a precision viticulture context are also highlighted. Hyperspectral sensors, UAVs, flight operations, and the processing methodology for each imaging type' datasets are presented through a qualitative and quantitative analysis. For this purpose, four vineyards in two countries were selected as case studies. This supports the extrapolation of both advantages and issues related with the two types of hyperspectral sensors used, in different contexts. Sensors' performance was compared through the evaluation of field operations complexity, processing time and qualitative accuracy of the results, namely the quality of the generated hyperspectral mosaics. The results shown an overall excellent geometrical quality, with no distortions or overlapping faults for both technologies, using the proposed mosaicking process and reconstruction. By resorting to the multi-site assessment, the qualitative and quantitative exchange of information throughout the UAV hyperspectral community is facilitated. In addition, all the major benefits and drawbacks of each hyperspectral sensor regarding its operation and data features are identified. Lastly, the operational complexity in the context of precision agriculture is also presented.

Real-time air concentrations and turbulent fluxes of volatile organic compounds (VOCs) over historic closed landfills to assess their potential environmental impact.

For the first time, emission/deposition fluxes of volatile organic compounds (VOCs) and H2S from a historic closed landfill site in Southern Italy were determined by Eddy Covariance (EC) using Proton Transfer Reaction Time-of-Flight Mass Spectrometry (PTR-TOF-MS). This was done in two field campaigns of one week performed in July and October 2016, where fluxes of CO2 and CH4 were also measured. Many compounds not previously identified in the biogas were detected by PTR-TOF-MS, but only in July some of them produced positive fluxes exceeding the flux limit of detection. Methanol was the most emitted compound with an average flux of 44.20 ± 4.28 µg m-2 h-1, followed by toluene with a mean flux of 18.97 ± 2.47 µg m-2 h-1. Toluene fluxes were 10 times higher than those of benzene, fitting rather well with values previously measured in the biogas. VOCs emission fluxes of monoterpenes and highly reactive arenes did not reflect, however, the biogas composition. This, combined with tiny emissions of VOC oxidation products, suggests that landfill emissions underwent some photochemical degradation before being dispersed in the atmospheric boundary layer (ABL). Deposition fluxes of some VOCs emitted from the sea was also observed in July. No relevant VOC fluxes were instead measured in October, suggesting that temperature was the variable controlling most landfill emission. Albeit small, summer landfill emissions from the investigated site can have an impact on the population living nearby, because they contain or still generate compounds that causing nuisance.

Overall Exposure of European Adult Population to Mycotoxins by Statistically Modelled Biomonitoring Data.

This study presents the exposure scenario to mycotoxins of adult population throughout Europe. The urinary biomarkers values were obtained by modelling data from two European projects. Exposure to AFB1, OTA, CIT, FBs, DON, NIV and T2/HT2 are presented. The main output obtained refers to a concern for public health about AFM1, FBs, T2/HT2 and NIV, and low concern for OTA, DON and CIT. The margin of exposure for AFM1 did not respect the reference value of 10,000 considered of low priority for risk; for Fusarium toxins, FBs and T2/HT2, probable daily intake (PDI) values resulted about ten times higher than their tolerable daily intake and NIV presented the most critical situation with a calculated PDI 30 times higher than the reference TDI value. North and South Europe scenarios were also depicted by clustering biomonitoring data. OTA and DON showed to be prevalent in Northern countries and the opposite was noticed for ZEN, higher in Southern countries. The critical issues of the availability of records feeding the dataset and of the accuracy of excretion rate for some mycotoxins are source of uncertainty for the reliability of the outputs, nevertheless the time is ripe for asking for more concrete HBM values and/or HBM-HBGV which would help in interpreting the burden of mycotoxins in Europe.

Predicted Aflatoxin B1 Increase in Europe Due to Climate Change: Actions and Reactions at Global Level.

Climate change (CC) is predicted to increase the risk of aflatoxin (AF) contamination in maize, as highlighted by a project supported by EFSA in 2009. We performed a comprehensive literature search using the Scopus search engine to extract peer-reviewed studies citing this study. A total of 224 papers were identified after step I filtering (187 + 37), while step II filtering identified 25 of these papers for quantitative analysis. The unselected papers (199) were categorized as "actions" because they provided a sounding board for the expected impact of CC on AFB1 contamination, without adding new data on the topic. The remaining papers were considered as "reactions" of the scientific community because they went a step further in their data and ideas. Interesting statements taken from the "reactions" could be summarized with the following keywords: Chain and multi-actor approach, intersectoral and multidisciplinary, resilience, human and animal health, and global vision. In addition, fields meriting increased research efforts were summarized as the improvement of predictive modeling; extension to different crops and geographic areas; and the impact of CC on fungi and mycotoxin co-occurrence, both in crops and their value chains, up to consumers.

The role of emissions and meteorology in driving CO2 concentrations in urban areas.

A multi-year dataset of measurements of CO2 concentrations, eddy covariance fluxes, and meteorological parameters over the city centre of Florence (Italy) has been analysed to assess the role of anthropogenic emissions and meteorology in controlling urban CO2 concentrations. The latter exhibited a negative correlation with air temperature, wind speed, solar radiation, and sensible heat flux and a positive one with relative humidity and emissions. A linear and an artificial neural network (ANN) model have been developed and validated for short-term modelling of 3-h CO2 concentrations. The ANN model performed better, with mean bias of 0.58 ppm, root mean square error within 30 ppm, and r2=0.49. Data clustering through the self-organized maps allowed to disentangle the role played by emissions and meteorological parameters in influencing CO2 concentrations. Sensitivity analysis of CO2 concentrations revealed a primary role played by the meteorological parameters, particularly wind speed. These results highlighted that (i) emission reduction actions at local urban scale should be better tied to actual and expected meteorological conditions and (ii) those actions alone have limited effects (e.g. a 20% emission reduction would result in a 3% CO2 concentrations reduction). For all these reasons, large-scale policies would be needed.

Occurrence and Co-Occurrence of Mycotoxins in Cereal-Based Feed and Food.

Dietary (co)-exposure to mycotoxins is associated with human and animal health concerns as well as economic losses. This study aims to give a data-based insight from the scientific literature on the (co-)occurrence of mycotoxins (i.e., parent and modified forms) in European core cereals, and to estimate potential patterns of co-exposure in humans and animals. Mycotoxins were mainly reported in wheat and maize showing the highest concentrations of fumonisins (FBs), deoxynivalenol (DON), aflatoxins (AFs), and zearalenone (ZEN). The maximum concentrations of FB1+FB2 were reported in maize both in feed and food and were above legal maximum levels (MLs). Similar results were observed in DON-food, whose max concentrations in wheat, barley, maize, and oat exceeded the MLs. Co-occurrence was reported in 54.9% of total records, meaning that they were co-contaminated with at least two mycotoxins. In the context of parental mycotoxins, co-occurrence of DON was frequently observed with FBs in maize and ZEN in wheat; DON + NIV and DON + T2/HT2 were frequently reported in barley and oat, respectively. Apart from the occurrence of ZEN and its phase I and phase II modified forms, only a limited number of quantified data were available for other modified forms; i.e., mainly the acetyl derivatives of DON. Data gaps are highlighted together with the need for monitoring studies on multiple mycotoxins to identify co-occurrence patterns for parent mycotoxins, metabolites, and their modified forms.

A Low-Cost and Unsupervised Image Recognition Methodology for Yield Estimation in a Vineyard.

Yield prediction is a key factor to optimize vineyard management and achieve the desired grape quality. Classical yield estimation methods, which consist of manual sampling within the field on a limited number of plants before harvest, are time-consuming and frequently insufficient to obtain representative yield data. Non-invasive machine vision methods are therefore being investigated to assess and implement a rapid grape yield estimate tool. This study aimed at an automated estimation of yield in terms of cluster number and size from high resolution RGB images (20 MP) taken with a low-cost UAV platform in representative zones of the vigor variability within an experimental vineyard. The flight campaigns were conducted in different light conditions and canopy cover levels for 2017 and 2018 crop seasons. An unsupervised recognition algorithm was applied to derive cluster number and size, which was used for estimating yield per vine. The results related to the number of clusters detected in different conditions, and the weight estimation for each vigor zone are presented. The segmentation results in cluster detection showed a performance of over 85% in partially leaf removal and full ripe condition, and allowed grapevine yield to be estimated with more than 84% of accuracy several weeks before harvest. The application of innovative technologies in field-phenotyping such as UAV, high-resolution cameras and visual computing algorithms enabled a new methodology to assess yield, which can save time and provide an accurate estimate compared to the manual method.

Development of Low-Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of PM2.5 and PM10 Sensors.

A low-cost air quality station has been developed for real-time monitoring of main atmospheric pollutants. Sensors for CO, CO2, NO2, O3, VOC, PM2.5 and PM10 were integrated on an Arduino Shield compatible board. As concerns PM2.5 and PM10 sensors, the station underwent a laboratory calibration and later a field validation. Laboratory calibration has been carried out at the headquarters of CNR-IBIMET in Florence (Italy) against a TSI DustTrak reference instrument. A MATLAB procedure, implementing advanced mathematical techniques to detect possible complex non-linear relationships between sensor signals and reference data, has been developed and implemented to accomplish the laboratory calibration. Field validation has been performed across a full "heating season" (1 November 2016 to 15 April 2017) by co-locating the station at a road site in Florence where an official fixed air quality station was in operation. Both calibration and validation processes returned fine scores, in most cases better than those achieved for similar systems in the literature. During field validation, in particular, for PM2.5 and PM10 mean biases of 0.036 and 0.598 µg/m³, RMSE of 4.056 and 6.084 µg/m³, and R² of 0.909 and 0.957 were achieved, respectively. Robustness of the developed station, seamless deployed through a five and a half month outdoor campaign without registering sensor failures or drifts, is a further key point.

Composition and emission of VOC from biogas produced by illegally managed waste landfills in Giugliano (Campania, Italy) and potential impact on the local population.

The composition in Volatile Organic Compounds (VOC) of the biogas produced by seven landfills of Giugliano (Naples, Campania, Italy) was determined and VOC emission rates assessed to verify if these compounds represent a potential threat to the population living nearby. VOC composition in the biogas could not be predicted, as heterogeneous waste was dumped from the late 1980s to the early 2000s and then underwent biological degradation. No data are available on the amount and composition of VOC in the biogas before the landfills closure as no operational biogas collection system was present. In this study, VOC composition was determined by gas chromatography-mass spectrometry (GC-MS), after collecting samples from collection pipes and from soil fractures in cover soil or capping. Individual VOC were quantified and data compared with those collected at two landfills in Latium, when they were still in operation. Relevant differences were observed, mainly due to waste aging, but no specific VOC revealing toxic waste dumping was found, although the concurrent presence of certain compounds suggested that dumping of industrial wastes might have occurred. The average VOC emission was assessed and a dispersion model was run to find out if the emitted plume could affect the health of population. The results suggested that fugitive emissions did not represent a serious danger, since the concentrations simulated at the neighboring cities were below the threshold limits for acute and chronic diseases. However, VOC plume could cause annoyance at night when the steady state conditions of the atmosphere enhance pollutants accumulation in the lower layers. In addition, some of the emitted VOC, such as alkylbenzenes and monoterpenes, can contribute to tropospheric ozone formation.

Industrial point source CO2 emission strength estimation with aircraft measurements and dispersion modelling.

CO2 remains the greenhouse gas that contributes most to anthropogenic global warming, and the evaluation of its emissions is of major interest to both research and regulatory purposes. Emission inventories generally provide quite reliable estimates of CO2 emissions. However, because of intrinsic uncertainties associated with these estimates, it is of great importance to validate emission inventories against independent estimates. This paper describes an integrated approach combining aircraft measurements and a puff dispersion modelling framework by considering a CO2 industrial point source, located in Biganos, France. CO2 density measurements were obtained by applying the mass balance method, while CO2 emission estimates were derived by implementing the CALMET/CALPUFF model chain. For the latter, three meteorological initializations were used: (i) WRF-modelled outputs initialized by ECMWF reanalyses; (ii) WRF-modelled outputs initialized by CFSR reanalyses and (iii) local in situ observations. Governmental inventorial data were used as reference for all applications. The strengths and weaknesses of the different approaches and how they affect emission estimation uncertainty were investigated. The mass balance based on aircraft measurements was quite succesful in capturing the point source emission strength (at worst with a 16% bias), while the accuracy of the dispersion modelling, markedly when using ECMWF initialization through the WRF model, was only slightly lower (estimation with an 18% bias). The analysis will help in highlighting some methodological best practices that can be used as guidelines for future experiments.

Influence of road traffic, residential heating and meteorological conditions on PM10 concentrations during air pollution critical episodes.

The importance of road traffic, residential heating and meteorological conditions as major drivers of urban PM10 concentrations during air pollution critical episodes has been assessed in the city of Florence (Italy) during the winter season. The most significant meteorological variables (wind speed and atmospheric stability) explained 80.5-85.5% of PM10 concentrations variance, while a marginal role was played by major emission sources such as residential heating (12.1%) and road traffic (5.7%). The persistence of low wind speeds and unstable atmospheric conditions was the leading factor controlling PM10 during critical episodes. A specific PM10 critical episode was analysed, following a snowstorm that caused a "natural" scenario of 2-day dramatic road traffic abatement (-43%), and a massive (up to +48%) and persistent (8 consecutive days) increase in residential heating use. Even with such a strong variability in local PM10 emissions, the role of meteorological conditions was prominent, revealing that short-term traffic restrictions are insufficient countermeasures to reduce the health impacts and risks of PM10 critical episodes, while efforts should be made to anticipate those measures by linking them with air quality and weather forecasts.

Urban-hazard risk analysis: mapping of heat-related risks in the elderly in major Italian cities.

BACKGROUND: Short-term impacts of high temperatures on the elderly are well known. Even though Italy has the highest proportion of elderly citizens in Europe, there is a lack of information on spatial heat-related elderly risks. OBJECTIVES: Development of high-resolution, heat-related urban risk maps regarding the elderly population (≥ 65). METHODS: A long time-series (2001-2013) of remote sensing MODIS data, averaged over the summer period for eleven major Italian cities, were downscaled to obtain high spatial resolution (100 m) daytime and night-time land surface temperatures (LST). LST was estimated pixel-wise by applying two statistical model approaches: 1) the Linear Regression Model (LRM); 2) the Generalized Additive Model (GAM). Total and elderly population density data were extracted from the Joint Research Centre population grid (100 m) from the 2001 census (Eurostat source), and processed together using "Crichton's Risk Triangle" hazard-risk methodology for obtaining a Heat-related Elderly Risk Index (HERI). RESULTS: The GAM procedure allowed for improved daytime and night-time LST estimations compared to the LRM approach. High-resolution maps of daytime and night-time HERI levels were developed for inland and coastal cities. Urban areas with the hazardous HERI level (very high risk) were not necessarily characterized by the highest temperatures. The hazardous HERI level was generally localized to encompass the city-centre in inland cities and the inner area in coastal cities. The two most dangerous HERI levels were greater in the coastal rather than inland cities. CONCLUSIONS: This study shows the great potential of combining geospatial technologies and spatial demographic characteristics within a simple and flexible framework in order to provide high-resolution urban mapping of daytime and night-time HERI. In this way, potential areas for intervention are immediately identified with up-to-street level details. This information could support public health operators and facilitate coordination for heat-related emergencies.

Aircraft mass budgeting to measure CO2 emissions of Rome, Italy.

Aircraft measurements were used to estimate the CO2 emission rates of the city of Rome, assessed against high-resolution inventorial data. Three experimental flights were made, composed of vertical soundings to measure Planetary Boundary Layer (PBL) properties, and circular horizontal transects at various altitudes around the city area. City level emissions and associated uncertainties were computed by means of mass budgeting techniques, obtaining a positive net CO2 flux of 14.7 ± 4.5, 2.5 ± 1.2, and 10.3 ± 1.2 µmol m(-2) s(-1) for the three flights. Inventorial CO2 fluxes at the time of flights were computed by means of spatial and temporal disaggregation of the gross emission inventory, at 10.9 ± 2.5, 9.6 ± 1.3, and 17.4 ± 9.6 µmol m(-2) s(-1). The largest differences between the two dataset are associated with a greater variability of wind speed and direction in the boundary layer during measurements. Uncertainty partitioned into components related to horizontal boundary flows and top surface flow, revealed that the latter dominates total uncertainty in the presence of a wide variability of CO2 concentration in the free troposphere (up to 7 ppm), while it is a minor term with uniform tropospheric concentrations in the study area (within 2 ppm). Overall, we demonstrate how small aircraft may provide city level emission measurements that may integrate and validate emission inventories. Optimal atmospheric conditions and measurement strategies for the deployment of aircraft experimental flights are finally discussed.

Carbon dioxide and acetone air-sea fluxes over the southern Atlantic.

Measurements of CO2 and acetone fluxes have been made over a large-scale, naturally occurring high latitude phytoplankton bloom in the remote South Atlantic. Shipborne micrometeorological methods for direct atmospheric flux measurement have been applied to determine the direction and size of the CO2 and acetone fluxes. Previous results suggest that high latitude oligotrophic ocean regions are sinks of acetone, whereas high productivity regions are sources. The observed CO2 fluxes are into the ocean and on the order of 1 micromol m(-2) s(-1) at most. The acetone fluxes measured show a significant relationship with chlorophyll in the region of the phytoplankton bloom. Although the uncertainty is very high due to the very low signal-to-noise ratio, significant positive acetone mean fluxes of the order of 0.01 nmol m(-2) s(-1) have been observed in bloom areas, whereas near zero, negative, or highly variable low acetone fluxes have been measured elsewhere. Based on these results we estimate that the global acetone source from bloom affected areas is small in comparison to the uptake from the much larger oligotrophic regions, and that the ocean is globally a net sink for acetone.