Environmental antimicrobial contamination from terraccumulation and diffuse pollution pathways.

Stephen J Rooklidge

doi:10.1016/j.scitotenv.2003.11.007

Home/Archive/PubMed

PubMed · 15144773

Environmental antimicrobial contamination from terraccumulation and diffuse pollution pathways.

Stephen J Rooklidge

View original source ↗Export metadata ↓

Abstract

The fate of antimicrobial pharmaceuticals entering the aquatic environment has become an increasing concern for researchers and regulators in the past decade, and recent research has focused on antimicrobial contamination from point sources, such as wastewater treatment facility outfalls. Terraccumulation is the concentration of pollutants in soils from land application of contaminated biosolids generated by agricultural practices and water and wastewater facilities. The terraccumulation of antimicrobials and mobility in diffuse pollution pathways should not be overlooked as a contributor to the spread of bacterial resistance and the resulting threat to human drug therapy. This review critically examines recent global trends of bacterial resistance, antimicrobial contaminant pathways from agriculture and water treatment processes, and the need to incorporate diffuse pathways into risk assessment and treatment system design. Alignment of environmental scientific and engineering research with strategies applied in clinical situations could contribute to continued efficacy of antimicrobial therapies necessary for human health and welfare.

Explore related subjects

Agriculture Animals Anti-Bacterial Agents Conservation of Natural Resources Diffusion Drug Resistance, Microbial Engineering Humans Risk Assessment Soil Pollutants Waste Disposal, Fluid Water Movements Water Pollutants, Chemical Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.Review

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Stephen J Rooklidge. 2004-06-05. Environmental antimicrobial contamination from terraccumulation and diffuse pollution pathways.. https://doi.org/10.1016/j.scitotenv.2003.11.007

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

Plant-derived and microbial biostimulants in sustainable agriculture: mechanisms, applications, and challenges.

Plant biostimulants have emerged as transformative and sustainable tools for improving crop productivity, resource-use efficiency, and resilience under rapidly intensifying environmental stresses. Unlike conventional agrochemicals, biostimulants function by activating physiological, biochemical, and molecular processes that optimize plant performance without directly supplying nutrients or exerting pesticidal effects. This review comprehensively examines the integrated roles of plant-derived and microbial biostimulants in sustainable agriculture, with particular emphasis on microbial-mediated mechanisms underlying plant stress adaptation and rhizosphere functioning. Plant-derived biostimulants, including seaweed extracts, humic substances, protein hydrolysates, amino acids, and chitosan, enhance nutrient acquisition, root architecture, hormonal regulation, and antioxidant defense systems. More importantly, microbial biostimulants, such as plant growth-promoting rhizobacteria (PGPR), endophytic microorganisms, mycorrhizal fungi, actinomycetes, yeasts, and cyanobacteria, exert multifunctional effects through biological nitrogen fixation, mineral solubilization, phytohormone biosynthesis, volatile signaling, osmolyte accumulation, pathogen suppression, and modulation of stress-responsive genes. These beneficial microorganisms reshape rhizosphere microbial communities, improve nutrient cycling, and enhance plant tolerance to drought, salinity, heat, and heavy metal toxicity. Emerging evidence from genomics, transcriptomics, metabolomics, and microbiome-based investigations has further revealed the molecular networks and signaling pathways governing biostimulant-induced resilience and plant-microbe interactions. Despite their substantial promise, inconsistent field performance, formulation instability, regulatory limitations, and inadequate mechanistic understanding continue to restrict their large-scale adoption. This review highlights recent advances in microbial and plant-derived biostimulants while identifying critical knowledge gaps and future opportunities for precision biostimulant engineering, microbiome manipulation, and climate-resilient crop management. The integration of next generation biostimulant technologies into sustainable agricultural systems may significantly reduce dependence on agrochemicals while improving crop productivity, environmental sustainability, and global food security.

Agriculture↗

PubMed2026

Data-driven approaches in green microbiology: strategies for plant growth-promoting bacteria.

Plant growth-promoting bacteria (PGPB) are gaining attention as scalable biological solutions to enhance crop productivity and resilience. However, accurately identifying and characterizing PGPB remains challenging, particularly under variable environmental conditions where microbial functions are context-dependent and shaped by complex plant-microbe interactions. Advances in high-throughput sequencing have shifted the field from culture-dependent approaches to genome-informed strategies, enabling large-scale taxonomic and functional profiling. Although trait-based databases support the prediction of plant-beneficial genes, they capture only a fraction of the underlying biological complexity and often require labor-intensive analyses. Machine learning (ML) and deep learning (DL) have emerged as powerful tools to integrate genomic, physiological, and ecological data, enabling the prioritization of candidate strains with plant growth-promoting potential. To evaluate advances in the field, we conducted a systematic review of studies integrating ML and DL with PGPB characterization, assessing algorithm selection, performance, and target plant systems. Across 248 observations, only 6.0% of studies directly addressed PGPB screening, whereas the majority (77.4%) focused on plant disease detection, revealing a substantial gap in the application of AI to beneficial microorganisms for plant growth. Convolutional neural networks (CNNs) were the most frequently applied algorithms, largely driven by image-based phenotyping tasks. Overall, the field is constrained by limited datasets, high computational demands, and challenges in modeling multispecies and host-associated interactions. We highlight the need for integrative and interpretable ML and DL frameworks that bridge genomic data and functional validation. Such approaches represent a promising path toward scalable, data-driven discovery and deployment of bioinoculants in sustainable agriculture.

Agriculture↗

PubMed2026

Artificial intelligence-driven advancements in agricultural biotechnology.

The need for faster and more informative data processing for better decision-making is driving the adoption of artificial intelligence (AI) in the agricultural sector. Thanks to recent advancements in computer science and the increase in computational powers of modern computers, AI is not only augmenting traditional solutions, but also helping in developing novel solutions to existing challenging matters. AI-driven models have an exceptional ability to identify patterns and combine a diverse collection of data together and make inference. The increasing pressure on farmlands posed by the growing global population and climate change is lessening growth, yield, and productivity ultimately posing risk to food security worldwide. Incorporation of AI in agriculture has the potential to drive farming efficiency to new heights. This comprehensive review critically evaluates the evolution of AI in agricultural biotechnology from a theoretical concept to a global phenomenon. A comprehensive literature search was performed using major scientific databases, including PubMed, Web of Science, Embase, Scopus, Lens and the Cochrane Library. In this review, we empirically demonstrate the fields advancement toward more capable AI systems and discuss the current applications of AI across crop improvement and precision agriculture such as crop improvement and genetic engineering, genomic selection and plant breeding, pest and disease detection, precision agriculture and smart farming, soil health and nutrient management, climate resilient crop development, livestock biotechnology, challenges and ethical considerations in AI based agricultural biotechnology. Furthermore, this review addresses the exponential growth of commercial intellectual property in the field and contrast it with academic publication outputs. Finally, we critically assess the ethical challenges impeding equitable adoption of AI including data sovereignty and digital divide, while projecting future frontiers involving quantum computing. This review will help build sustainable agricultural systems capable of adapting to climate change, contribute to the development of climate-resilient and high-yielding crops, and address global food security challenges.

Agriculture↗