The negative influence of agrochemicals (pesticides: insecticide, fungicide, and herbicide) on biodiversity is a major ecological concern. In recent decades, many insect species are reported to have rapidly declined worldwide, and pesticides, including neonicotinoids and fipronil, are suspected to be partially responsible. In Japan, application of systemic insecticides to nursery boxes in rice paddies is considered to have caused rapid declines in Sympetrum (Odonata: Libellulidae) and other dragonfly and damselfly populations since the 1990s. In addition to the direct lethal effects of pesticides, agrochemicals indirectly affect Odonata populations through reductions in macrophytes, which provide a habitat, and prey organisms. Due to technical restrictions, most previous studies first selected target chemicals and then analyzed their influence on focal organisms at various levels, from the laboratory to the field. However, in natural and agricultural environments, various chemicals co-occur and can act synergistically. Under such circumstances, targeted analyses might lead to spurious correlations between a target chemical and the abundance of organisms. To address such problems, in this study we adopted a novel technique, “Comprehensive Target Analysis with an Automated Identification and Quantification System (CTA-AIQS)” to detect wide range of agrochemicals in water environment. The relationships between a wide range of pesticides and lentic Odonata communities were surveyed in agricultural and non-agricultural areas in Saga Plain, Kyushu, Japan. We detected significant negative relationships between several insecticides, i.e., acephate, clothianidin, dinotefuran, flubendiamide, pymetrozine, and thiametoxam (marginal for benthic odonates) and the abundance of lentic Epiprocta and benthic Odonates. In contrast, the herbicides we detected were not significantly related to the abundance of aquatic macrophytes, suggesting a lower impact of herbicides on aquatic vegetation at the field level. These results highlight the need for further assessments of the influence of non-neonicotinoid insecticides on aquatic organisms.

Microplastic (MP) contamination is present in the entire marine environment from the sediment to the water surface and down to the deep sea. This ubiquitous presence of MP particles opens the possibility for their ingestion by nearly all species in the marine ecosystem. Reports have shown that MP particles are present in local commercial seafood species leading to the possible human ingestion of these particles. However, due to a lack of harmonized methods to identify microplastics (MPs), results from different studies and locations can hardly be compared. Hence, this study was aimed to detect, quantify, and estimate MP contamination in commercially important mussels originating from 12 different countries distributed worldwide. All mussels were obtained from supermarkets and were intended for human consumption. Using a combinatorial approach of focal plane array (FPA)-based micro- Fourier-transform infrared (FTIR) spectroscopy and micro-Raman spectroscopy allowed the detection and characterization of MP down to a size of 3 μm in the investigated mussels. Further, a gentle sample purification method based on enzymes has been modified in order to optimize the digestion of organic material in mussels. A random forest classification (RFC) approach, which allows a rapid discrimination between different polymer types and thus fast generation of data on MP abundance and size distributions with high accuracy, was implemented in the analytical pipeline for IR spectra. Additionally, for the first time we also applied a RFC approach for the automated characterization of Raman spectra of MPs.

Nitric oxide (NO) plays a critical role in atmospheric chemistry and also is a precursor of nitrate, which affects particle matter formation and nitrogen deposition. Agricultural soil has been recognized as a main source of atmospheric NO. However, quantifying the NO fluxes emitted from croplands remains a challenge and in situ long-term measurements of NO are still limited. In this study, we used an automated sampling system to measure NO fluxes with a high temporal resolution over two years (April 2017 to March 2019) from a rainfed maize field in the Northeast China. The cumulative annual NO emissions were 8.9 and 2.3 kg N ha⁻¹ in year 1 (April 2017 to March 2018) and year 2 (April 2018 to March 2019), respectively. These interannual differences were largely related to different weather conditions encountered. In year 1, a month-long drought before and after the seeding and fertilizing reduced plant N uptake and dramatically increased soil N concentration. The following moderate rainfalls promoted large amount of NO emissions, which remained high until late September. The NO fluxes in both years showed clearer seasonal patterns, being highest after fertilizer application in summer, and lowest in winter. The seasonal patterns of NO fluxes were mainly controlled by soil available N concentrations and soil temperatures. The contribution of NO fluxes during the spring freeze-thaw in both years was no more than 0.2% of the annual NO budget, indicating that the freeze-thaw effect on agricultural NO emissions was minimal. In addition, with high-resolution monitoring, we found that soil not only act as a NO source but also a sink. Long-term and high-resolution measurements help us better understand the diurnal, seasonal, and annual dynamics of NO emissions, build more accurate models and better estimate global NO budget and develop more effective policy responses to global climate change.

Artificial light at night (ALAN) is spreading worldwide and thereby is increasingly interfering with natural dark-light cycles. Meanwhile, effects of very low intensities of light pollution on animals have rarely been investigated. We explored the effects of low intensity ALAN over seven months in eight experimental bank vole (Myodes glareolus) populations in large grassland enclosures over winter and early breeding season, using LED garden lamps. Initial populations consisted of eight individuals (32 animals per hectare) in enclosures with or without ALAN. We found that bank voles under ALAN experienced changes in daily activity patterns and space use behavior, measured by automated radiotelemetry. There were no differences in survival and body mass, measured with live trapping, and none in levels of fecal glucocorticoid metabolites. Voles in the ALAN treatment showed higher activity at night during half moon, and had larger day ranges during new moon. Thus, even low levels of light pollution as experienced in remote areas or by sky glow can lead to changes in animal behavior and could have consequences for species interactions.

Microplastics represent an emerging environmental issue and have been found almost everywhere including seafood, raising a great concern about the ecological and human health risks they pose. This study addressed the common technical challenges in the assessment of microplastics in seafood by developing an improved protocol based on Raman spectroscopy and using the green-lipped mussel Perna viridis and the Japanese jack mackerel Trachurus japonicus as the test models. Our findings identified a type of stainless-steel filter membranes with minimal Raman interference, and a combination of chemicals that achieved 99–100% digestion efficiency for both organic and inorganic biomass. This combined chemical treatment reached 90–100% recovery rates for seven types of microplastics, on which the surface modification was considered negligible and did not affect the accuracy of polymer identification based on Raman spectra, which showed 94–99% similarity to corresponding untreated microplastics. The developed extraction method for microplastics was further combined with an automated Raman mapping approach, from which our results confirmed the presence of microplastics in P. viridis and T. japonicus collected from Hong Kong waters. Identified microplastics included polypropylene, polyethylene, polystyrene and poly(ethylene terephthalate), mainly in the form of fragments and fibres. Our protocol is applicable to other biological samples, and provides an improved alternative to streamline the workflow of microplastic analysis for routine monitoring purposes.

Between 9 March and 18 May 2020, strict lockdown measures were adopted in Italy for containing the COVID-19 pandemic: in Rome, despite vehicular traffic on average was more than halved, it was not observed a evident decrease of the airborne particulate matter (PM) concentrations, as assessed by air quality data. In this study, daily PM₁₀ filters were collected from selected automated stations operated in Rome by the regional network of air quality monitoring: their magnetic properties – including magnetic susceptibility, hysteresis parameters and FORC (first order reversal curves) diagrams - were compared during and after the lockdown, for outlining the impact of the COVID-19 measures on airborne particulate matter. In urban traffic sites, the PM₁₀ concentrations did not significantly change after the end of the lockdown, when vehicular traffic promptly returned to its usual levels; conversely, the average volume and mass magnetic susceptibilities approximately doubled, and the linear correlation between volume magnetic susceptibility and PM₁₀ concentration became significant, pointing out the link between PM₁₀ concentrations and the increasing levels of traffic-related magnetic emissions. Magnetite-like minerals, attributed to non-exhaust brakes emissions, dominated the magnetic fraction of PM₁₀ near urban traffic sites, with natural magnetic components emerging in background sites and during exogenous dusts atmospheric events. Magnetic susceptibility constituted a fast and sensitive proxy of vehicular particulate emissions: the magnetic properties can play a relevant role in the source apportionment of PM₁₀, especially when unsignificant variations in its concentration levels may mask important changes in the traffic-related magnetic fraction. As a further hint, increasing attention should be drawn to the reduction of brake wear emissions, that are overcoming by far fuel exhausts as the main particulate pollutant in traffic contexts.

Organophosphate esters (OPEs) are high-production volume chemicals. Use of OPEs has largely increased since the phase-out/ban of polybrominated diphenyl ethers (PBDEs). The ubiquitous occurrence of OPEs, in higher concentrations in abiotic matrices than brominated flame retardants (BFRs), is a concern because several of the OPEs have been linked to adverse health effects. In this study, urinary metabolites of OPEs were measured in a subset of a population-based sample of women of child bearing age recruited in Ontario, and associations between serum lipid levels and urinary concentrations of OPE metabolites were evaluated. Urine samples (n = 120) were extracted using automated solid phase extraction and analysed by ultra-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS). Diphenyl phosphate (DPHP), bis(2-chloropropyl) phosphate (BCIPP) and bis(1,3-dichloro-2 propyl) phosphate (BDCIPP) were detected with frequencies of 100%, 76% and 75% at median concentrations of 13.8 ng/mL, 0.5 ng/mL and 1.8 ng/mL, respectively. Bis(2-chloroethyl) hydrogen phosphate (BCEP) and di-cresyl phosphate (DCP; mixture of 3 isomers) were detected in 52% and 42% of the samples, respectively. Detected at lower frequencies were 1-hydroxy-2-propyl bis(1-chloro-2-propyl) phosphate (BCIPHIPP, 29%), bis-2(butoxyethyl) phosphate (BBOEP, 11%), and desbutyl-tris-(2-butoxy-ethyl) phosphate (desbutyl TBOEP, 9%). Using multiple regression model, a negative statistically significant correlation was observed between BCEP and cholesterol (p = 0.04), as well as BCEP and total lipid (p = 0.04). Whereas BCIPP was positively and significantly correlated with cholesterol (p = 0.003) and LDL (p = 0.001). Additional work to further explore these relationships and to evaluate more recently identified OPE metabolites is warranted.

Topsoils near active and abandoned mining and smelting sites are highly polluted by metal(loid) contaminants, which are often bound to particulates emitted from ore processing facilities and/or windblown from waste disposal sites. To quantitatively determine the contaminant partitioning in the soil particulates, we tested an automated mineralogy approach on the heavy mineral fraction extracted from the mining- and smelting-polluted topsoils exhibiting up to 1920 mg/kg As, 5840 mg/kg Cu, 4880 mg/kg Pb and 3310 mg/kg Zn. A new generation of automated scanning electron microscopy (autoSEM) was combined and optimized with conventional mineralogical techniques (XRD, SEM/EDS, EPMA). Parallel digestions and bulk chemical analyses were used as an independent control of the autoSEM-calculated concentrations of the key elements. This method provides faster data acquisition, the full integration of the quantitative EDS data and better detection limits for the elements of interest. We found that As was mainly bound to the apatite group minerals, slag glass and metal arsenates. Copper was predominantly hosted by the sulfides/sulfosalts and the Cu-bearing secondary carbonates. The deportment of Pb is relatively complex: slag glass, Fe and Mn (oxyhydr)oxides, metal arsenates/vanadates and cerussite were the most important carriers for Pb. Zinc is mainly bound to the slag glass, Fe (oxyhydr)oxides, smithsonite and sphalerite. Limitations exist for the less abundant contaminants, which cannot be fully quantified by autoSEM due to spectral overlaps with some major elements (e.g., Sb vs. Ca, Cd vs. K and Ca in the studied soils). AutoSEM was found to be a useful tool for the determination of the modal phase distribution and element partitioning in the metal(loid)-bearing soil particulates and will definitely find more applications in environmental soil sciences in the future.

The increasing level of marine plastic pollution poses severe threats to the marine ecosystem and biodiversity. Open remote sensing data and advanced machine learning (ML) algorithms could be a cost-effective solution for identifying large plastic patches across the scale. The potential application of such resources in detecting and discriminating marine floating plastics (MFP) are not fully explored. Therefore, the present study attempted to explore the full functionality of open Sentinel satellite data and ML models for detecting and classifying the MFP in Mytilene (Greece), Limassol (Cyprus), Skala Loutron, Greece, Calabria (Italy), and Beirut (Lebanon). Two ML models, i.e. Support Vector Machine (SVM) and Random Forest (RF), were utilized to perform the classification analysis. In-situ plastic location data was collected from the control experiments conducted in Mytilene, Greece (in 2018 and 2019), Skala Loutron, Greece (2021), and Limassol, Cyprus (2018), and the same was considered for training the models. The accuracy and performances of the trained models were further tested on unseen new data collected from Calabria, Italy and Beirut, Lebanon. Both remote sensing bands and spectral indices were used for developing the ML models. A spectral signature profile for marine plastic was created for discriminating the floating plastic from other marine debris. A newly developed index, kernel Normalized Difference Vegetation Index (kNDVI), was incorporated into the modelling to examine its contribution to model performances. Both SVM and RF were performed well in five models and test case combinations. Among the two ML models, the highest performance was measured for the RF. The inclusion of kNDVI was found effective and increased the model performances, reflected by high balanced accuracy measured for model 2 (~89% to ~100% for SVM and ~92% to ~98% for RF). An automated floating plastic detection system was developed and tested in Calabria and Beirut using the best-performed model. The trained model had detected the floating plastic for both sites with ~80%–90%% accuracy. Among the six predictors, the Floating Debris Index (FDI) was the most important variable for detecting marine floating plastic. These findings collectively suggest that high-resolution remote sensing imagery and the automated ML models can be an effective alternative for the cost-effective detection of MFP. Future research will be directed toward collecting quality training data to develop robust automated models and prepare a spectral library for different plastic objects for discriminating plastic from other marine floating debris and advancing the marine plastic pollution research by taking full advantage of open-source data and technologies.