The role of food-producing environments in the emergence and spread of antimicrobial resistance (AMR) in EU plant-based food production, terrestrial animals (poultry, cattle and pigs) and aquaculture was assessed. Among the various sources and transmission routes identified, fertilisers of faecal origin, irrigation and surface water for plant-based food and water for aquaculture were considered of major importance. For terrestrial animal production, potential sources consist of feed, humans, water, air/dust, soil, wildlife, rodents, arthropods and equipment. Among those, evidence was found for introduction with feed and humans, for the other sources, the importance could not be assessed. Several ARB of highest priority for public health, such as carbapenem or extended-spectrum cephalosporin and/or fluoroquinolone-resistant Enterobacterales (including Salmonella enterica), fluoroquinolone-resistant Campylobacter spp., methicillin-resistant Staphylococcus aureus and glycopeptide-resistant Enterococcus faecium and E. faecalis were identified. Among highest priority ARGs bla CTX -M, bla VIM, bla NDM, bla OXA -48-like, bla OXA -23, mcr, armA, vanA, cfr and optrA were reported. These highest priority bacteria and genes were identified in different sources, at primary and post-harvest level, particularly faeces/manure, soil and water. For all sectors, reducing the occurrence of faecal microbial contamination of fertilisers, water, feed and the production environment and minimising persistence/recycling of ARB within animal production facilities is a priority. Proper implementation of good hygiene practices, biosecurity and food safety management systems is very important. Potential AMR-specific interventions are in the early stages of development. Many data gaps relating to sources and relevance of transmission routes, diversity of ARB and ARGs, effectiveness of mitigation measures were identified. Representative epidemiological and attribution studies on AMR and its effective control in food production environments at EU level, linked to One Health and environmental initiatives, are urgently required.

Role played by the environment in the emergence and spread of antimicrobial resistance (AMR) through the food chain

Non-conventional water reuse in agriculture: A circular water economy

Aquatic toxicity and aquatic ecological risk assessment of wastewater-derived halogenated phenolic disinfection byproducts

Aquatic toxicity and aquatic ecological risk assessment of wastewater-derived halogenated phenolic disinfection byproducts.

Wastewater reuse for irrigation of produce: A review of research, regulations, and risks.

Treated wastewater reuse for irrigation: Pros and cons

A resilient environment is essential for society's long-term viability. Receptor models have evolved into an excellent tool for detecting pollution sources and evaluating each source's empirical contributions based on ecological datasets. One hundred and fifteen soil sample were collected from the district of Frydek Mistek in the Czech Republic and the concentration of arsenic (As), cadmium (Cd), copper (Cu), chromium (Cr), manganese (Mn), nickel (Ni), lead (Pb)and zinc (Zn) measured inductively coupled plasma–optical emission spectrometry. The results suggested that the hybridized receptor models ER-PMF and PMF identified the following geogenic, steel industries, vehicular traffic, and agro-based activities such as pesticide and fertilizer applications as the primary sources in the source distribution. The ER-PMF source pollution identification efficiency ranged from R2 0.872–0.970, RMSE 0.128–17.344 and MAE 0.085–10.388, whereas the PMF R2 ranged from 0.883 to 0.960, RMSE 0.246–79.003 and MAE 0.145–49.925. The overall assessment of the efficiency of the receptor models suggests that the ER-PMF appears to yield more efficient results in pollution source identification compared to PMF. The PTEs mapping using geographical weighted regression (GWR) and a hybridized regression approach, geographical weighted regression cokriging (GWRCoK), revealed that GWRCoK had a higher goodness of fit in the spatial prediction maps than GWR. According to Hakanson's risk index classification, the ecological risk level in the study area was moderate to high (risk level = 51 observed locations out of 115, or 44.35%); however, Chen's risk index reclassification indicated that the toxicity level in the study area was moderate to extremely high (risk level = 113 observed locations out of 115, or 98.26%). However, the uncertainty assessment results indicated that the DISP interval ratio of the hybridized ER-PMF model was lower than that of the parent PMF model. However, it was clear that the random error that could occur in the DISP based on the DISP interval ratio was likely to be lower in the ER-PMF receptor model than in the parent model. The assessment of PTEs in soil has been widely published, but this study recommends using a pollution assessment-based receptor model (ER-PMF), which has been shown to be reliable and practical in estimating distribution sources. 

Ecological risk source distribution, uncertainty analysis, and application of geographically weighted regression cokriging for prediction of potentially toxic elements in agricultural soils


 Discharges from wastewater treatment plants have been cited as one of the point sources contributing to surface water quality deterioration. However, does high-quality effluent affect water quality, and contribute significantly to nutrient enrichment or the eutrophication of receiving waters? The Vltava River and a wastewater treatment plant in the Czech Republic were used in a case study, to try to answer these questions through water quality indices and source identification. Samples were collected upstream and downstream of the effluent discharge point, and analyzed for temperature, pH, dissolved oxygen, chemical oxygen demand, nitrate, nitrite, ammonium, phosphate and sulfate. No statistically significant difference (P > 0.05) was observed between most of the upstream and downstream samples’ physicochemical characteristics. The water quality, organic pollution, and eutrophication indices of the river, upstream of the effluent discharge point were 83.48, 2.05 and 2.67, respectively, but increased to 99.06, 2.87 and 3.74 downstream. Nutrient source identification using principal component analysis suggests that the increase might be due to the effluent discharge. However, the river's comprehensive ecological (quality classification) status was the same upstream as downstream, indicating that the discharged effluent did not cause nutrient enrichment of the river.

Solomon Ofori

Papers

Treated wastewater reuse for irrigation: Pros and cons

Ecological risk source distribution, uncertainty analysis, and application of geographically weighted regression cokriging for prediction of potentially toxic elements in agricultural soils

Assessing the influence of treated effluent on nutrient enrichment of surface waters using water quality indices and source apportionment