We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Improving monitoring of microplastics in soils and biosolids using near-infrared spectroscopy: the effect of organic matter
AI summary Read the abstract
Scientists tested whether a light-based scanning tool (near-infrared spectroscopy) can detect tiny plastic bits in soil and sewage sludge that's often used as farm fertilizer, even though the organic matter in these materials can make plastics harder to "see." Using smart computer models, they found this method accurately measured plastic contamination in both clean soil and nutrient-rich sludge, offering a faster, cheaper way to track microplastics before they end up in our food and water supply.
Microplastics (MPs) are emerging contaminants ubiquitously present in the environment, yet their rapid quantification in complex matrices such as soils and biosolids remains challenging because of spectral interference from organic matter (OM). This study investigates, for the first time, how high OM contents influence the spectral detectability and machine-learning-based quantification of high-density polyethylene (HDPE) and polystyrene (PS) polymers (at 0-8% v/v) in soils and biosolids. Pre-processed NIR spectra were analysed using partial least squares-discriminant analysis (PLS-DA) to identify MP- and OM-related wavelength regions via variable importance in projection (VIP), which were subsequently used as inputs for four regression algorithms (PLS, support vector machines, random forest and custom neural network). In soils with low OM (0.78-1.23%), HDPE and PS produced polymer-specific absorption features and were predicted with excellent accuracy (up to R 2 = 0.96 and residual prediction deviation, RPD = 5.19), using a relatively small set of VIP-selected wavelengths. In biosolids with very high OM (64-72%), polymer bands were partially masked, and VIP scores shifted towards OM-dominated regions. However, prediction performance remained robust, especially for HDPE with neural network model (R 2 = 0.98, RPD = 6.41). Overall, although the OM strongly influenced the spectral regions driving model predictions and partially reduced discrimination among MP concentration levels, it does not prevent accurate quantification of MPs when appropriate variable selection and machine-learning strategies were applied. These findings demonstrate that NIR spectroscopy coupled with targeted variable selection and advanced regression can provide robust, non-destructive estimation of MP contamination in OM-poor soils and OM-rich biosolids, highlighting key spectral features to prioritise in future studies targeting natural organic-rich samples with low-MP concentration.
More Papers Like This
Detection limits of soil microplastics using mid-infrared spectroscopy
AI summary Read the abstract
Scientists have developed a more sensitive way to detect tiny plastic particles hiding in farm soil, using a light-based scanning technique that works especially well in sandy and loamy soils (though less well in clay). This matters because microplastics in agricultural soil can potentially work their way into the crops we eat, so better detection tools are a key step toward understanding — and eventually limiting — our exposure to plastic pollution through food.
Influence of soil types with different soil-forming process on the qualitative and quantitative detection of microplastics by near-infrared spectroscopy
AI summary Read the abstract
Scientists tested whether a light-based scanning tool (near-infrared spectroscopy) can reliably detect tiny plastic bits in different types of farm soil, and found that soil type matters a lot. Some soils, like Brown Earth, made it easy to spot even small amounts of microplastics, while others, like Fluvo-aquic Soil, made detection much less accurate. This matters because microplastics in farmland can work their way into the food we eat, and having a fast, reliable way to detect them is a key step toward understanding and reducing that risk to human health.
Predicting soil microplastic concentration using vis-NIR spectroscopy
AI summary Read the abstract
Researchers used visible and near-infrared (vis-NIR) spectroscopy to predict microplastic concentrations in soil samples, developing calibration models that could estimate contamination levels directly from spectral measurements without extensive sample preparation. The approach offers potential for faster and more scalable monitoring of microplastic pollution in agricultural and natural soils.
Rapid Detection of Microplastics in Plastic-covered Soil Using FT-NIR and ATR-FTIR Spectral Data Fusion
AI summary Read the abstract
Scientists developed a new method to quickly detect tiny plastic particles in farm soil by combining two different light-based detection techniques. This method can accurately measure microplastic pollution in agricultural fields where plastic covers are used for growing crops. This matters because microplastics in farm soil can potentially enter our food chain through the fruits and vegetables we eat.
Rapid Detection of Microplastics in Plastic-covered Soil Using FT-NIR and ATR-FTIR Spectral Data Fusion
AI summary Read the abstract
Scientists developed a faster way to detect tiny plastic particles in farm soil by combining two different scanning methods. This new technique can accurately measure microplastic pollution in agricultural fields where plastic covers are used to help crops grow. This matters because microplastics in farm soil can potentially enter our food supply, so having better detection methods helps us monitor and control this type of pollution.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.