Bioindication of agroecosystem pollution: integrating biochemical analyses with proximal and remote sensing technologies

Chemical and Biological Technologies in Agriculture is calling for submissions to our Collection on Bioindication of agroecosystem pollution: integrating biochemical analyses with proximal and remote sensing technologies.

 Environmental contamination is a relevant problem on a global and local scale. The increasing social concern about environmental quality is driven by compelling evidence that the emission of harmful substances negatively impacts on natural ecosystems, human health, and agricultural productivity. The pollutants released by anthropogenic activities, such as trace elements, hydrocarbons and heavy metals, can be found in the whole biosphere transported by air, water and living organisms. These toxic chemicals become part of the natural biogeochemical cycle and accumulate in the food chain. They also affect humans, causing (directly or indirectly) various poisonings, toxicoses, and even neoplastic diseases. However, when the consequences of environmental pollution become visible, it is often too late to prevent them, hence investigations have recently focused on searching for bioindicators (both plant and animal organisms) that could signal the contamination of ecosystems by toxic substances. 

The term bioindicator usually refers to any organism or biological system used to evaluate a change - generally degenerative - in the environmental quality at any level of organization and utilization. Depending on the case, a bioindicator can be a community, a group of species with similar behavior (ecological group), a particularly sensitive species (indicator species) or a portion of an organism (such as organs, tissues, or even cells). Important parameters for an effective bioindicator include proven sensitivity towards the toxic substance being investigated, manifestation of visible and specific symptoms, widespread distribution in the survey area, limited mobility and a sufficiently long-life cycle.

Plant organisms own all these characteristics, thus representing real natural "sensors". Phytoindicators can be employed both in environmental quality analysis and environmental pollution assessment, as they allow to determine the rate, level and extent of current and future man-induced changes in natural ecosystems.

However, traditional biomonitoring methods for environmental issues suffer from several problematics. High costs often restrict the scale of coverage, especially in systems that rely on manual sampling. Additionally, traditional biomonitoring approaches tend to be specific to particular systems and stressors, lacking generality. Therefore, there is a growing interest in alternative techniques that can provide a large number of observations in a short period. In this context, proximal and remote sensing technologies have emerged as promising solutions for effective monitoring of phytoindicators in assessing environmental pollution. Proximal and remote sensing technologies represent a valid tool to complement the traditional in situ approach, and can be strategically combined with biochemical analysis to monitor the vulnerability of agricultural sites and anticipate environmental violations.

For these reasons, a multidisciplinary approach to bioindication and the strengthening of collaboration between the various scientific communities are considered useful in order to develop innovative and efficient technologies in the biomonitoring of environmental pollution. The overall objective of this series is to bring together studies that delve into the intricate connections between pollutants and their environmental effects, with an emphasis on exploring innovative techniques and approaches. 

This thematic series aims to consolidate research on various aspects of bioindication for environmental pollution, involving different scientific domains. Topics of interest for this Special Issue include but are not limited to the following areas:

• Fate and transport processes of contaminants from source to soil and water;
• Impact of toxic substances on ecosystems, human health and agricultural crops;
• Development of advanced technologies for the identification of pollutants in different environmental matrices;
• Statistical modelling and machine learning to correlate physiological analysis of phytoindicators and proximal/remote sensing data;
• Usage of active and passive sensors to bioindication and their synergies;
• Different bioindicators measured at different spatial scales (phenotyping platforms, drones and air-borne data);
• Biophysical parameters at different spatial scales;
• Uncertainty assessment of remote sensing and ground based data.