Impact of heavy precipitation events on pathogen occurrence in estuarine areas of the Puzi River in Taiwan

Deciphering assembly processes, network complexity and stability of potential pathogenic communities in two anthropogenic coastal regions of a highly urbanized estuary

The existence of potential pathogens may lead to severe water pollution, disease transmission, and the risk of infectious diseases, posing threats to the stability of aquatic ecosystems and human health. In-depth research on the dynamic of potential pathogenic communities is of significant importance, it can provide crucial support for assessing the health status of aquatic ecosystems, maintaining ecological balance, promoting sustainable economic development, and safeguarding human health. Nevertheless, the current understanding of the distribution and geographic patterns of potential pathogens in coastal ecosystems remains rather limited. Here, we investigated the diversity, assembly, and co-occurrence network of potential pathogenic communities in two anthropogenic coastal regions, i.e., the eight mouths (EPR) and nearshore region (NSE), of the Pearl River Estuary (PRE) and a total of 11 potential pathogenic types were detected. The composition and diversity of potential pathogenic communities exhibited noteworthy distinctions between the EPR and NSE, with 6 shared potential pathogenic families. Additionally, in the NSE, a significant pattern of geographic decay was observed, whereas in the EPR, the pattern of geographic decay was not significant. Based on the Stegen null model, it was noted that undominant processes (53.36%/69.24%) and heterogeneous selection (27.35%/25.19%) dominated the assembly of potential pathogenic communities in EPR and NSE. Co-occurrence network analysis showed higher number of nodes, a lower average path length and graph diameter, as well as higher level of negative co-occurrences and modularity in EPR than those in NSE, indicating more complex and stable correlations between potential pathogens in EPR. These findings lay the groundwork for the effective management of potential pathogens, offering essential information for ecosystem conservation and public health considerations in the anthropogenic coastal regions.

The effects of heavy rain on the fate of urban and agricultural pollutants in the riverside area around weirs using multi-isotope, microbial data and numerical simulation

The increase in extreme heavy rain due to climate change is a critical factor in the fate of urban and agricultural pollutants in aquatic system. Nutrients, including NO3- and PO43-, are transported with surface and seepage waters into rivers, lakes and aquifers and can eventually lead to algal blooms. δ15N-NO3-, δ18O-NO3-, and δ11B combined with hydrogeochemical and microbial data for groundwater and surface water samples were interpreted to evaluate the fate of nutrients in a riverside area around weirs in Daegu, South Korea. Most of the ions showed similar concentrations in the groundwater samples before and after heavy rain while concentrations of major ions in surface water samples were diluted after heavy rain. However, Si, PO43-, Zn, Ce, La, Pb, Cu and a number of waterborne pathogens increased in surface water after heavy rain. The interpretation of δ11B, δ15N-NO3-, and δ18O-NO3- values using a Bayesian mixing model revealed that sewage and synthetic fertilizers were the main sources of contaminants in the groundwater and surface water samples. δ18O and SiO2 interpreted using the Bayesian mixing model indicated that the groundwater component in the surface water increased from 4.4 % to 17.9 % during the wet season. This is consistent with numerical simulation results indicating that the direct surface runoff and the groundwater baseflow contributions to the river system had also increased 6.4 times during the wet season. The increase in proteobacteria and decrease of actinobacteria in the surface water samples after heavy rain were also consistent with an increase of surface runoff and an increased groundwater component in the surface water. This study suggests that source apportionment based on chemical and multi-isotope data combined with numerical modeling approaches can be useful for identifying main hydrological and geochemical processes in riverside areas around weirs and can inform suggestions of effective methods for water quality management.

Identifying macroplastic pathobiomes and antibiotic resistance in a subtropical fish farm

Macroplastics are ubiquitous in aquaculture ecosystems. However, to date the potential role of plastics as a support for bacterial biofilm that can include potential human pathogenic bacteria (PHPB) and antibiotic-resistant bacteria (ARB) has been largely overlooked. In this study, we used a combination of metabarcoding and standard antibiotic susceptibility testing to study the pathobiome and resistome of macroplastics, fish guts and the environment in a marine aquaculture farm in Mauritius. Aquaculture macroplastics were found to be higher in PHPB, dominated by the Vibrionaceae family (0.34 % of the total community), compared with environmental samples. Moreover, isolates from aquaculture plastics showed higher significant multiple antibiotic resistance (MAR) compared to non-plastic samples of seawater, sediment and fish guts. These results suggest that plastics act as a reservoir and fomite of PHPB and ARB in aquaculture, potentially threatening the health of farmed fish and human consumers.

Gulf Cooperation Council countries' water and climate research to strengthen UN's SDGs 6 and 13

Clean water and sanitation and climate actions represent two of the seventeen United Nations Sustainable Development Goals (SDGs). Although challenging, the two goals can be achieved by 2030 through unconventional and innovative solutions. Scientific research related to clean water and sanitation (SDG 6) and urgent actions to combat climate change and its impacts (SDG 13) will help develop new technologies to support the two goals and can bridge the gap between practitioners and academia's to achieve sustainability. The Gulf Cooperation Council (GCC) countries are located in an arid region. Their water and climate research activities and outcomes may provide a good contribution toward achieving the two goals. This study used text mining and bibliometric methods to analyze water and climate research contributions to achieve SDGs 6 and 13 in GCC countries. Results revealed that there is an increase in research publications after 2016 in the areas of water and climate in the GCC countries involving a longstanding international collaboration with developed countries. Research topics were focused on wastewater treatment, contamination, heavy metal, groundwater, and climate change impacts. Under SDG 6, most of the publications were research articles (77.3%), followed by reviews (11.1%), and the rest were book chapters and conference papers. For SDG 13, 75.1% of the publications are research articles, 10.9% are conference papers, and 8% are reviews. The research outcomes in the GCC countries have clearly contributed to the development of water and climate strategies and international collaborations to achieve the two goals.

Evaluating the Spatial Risk of Bacterial Foodborne Diseases Using Vulnerability Assessment and Geographically Weighted Logistic Regression

Foodborne diseases are an increasing concern to public health; climate and socioeconomic factors influence bacterial foodborne disease outbreaks. We developed an “exposure–sensitivity–adaptability” vulnerability assessment framework to explore the spatial characteristics of multiple climatic and socioeconomic environments, and analyzed the risk of foodborne disease outbreaks in different vulnerable environments of Zhejiang Province, China. Global logistic regression (GLR) and geographically weighted logistic regression (GWLR) models were combined to quantify the influence of selected variables on regional bacterial foodborne diseases and evaluate the potential risk. GLR results suggested that temperature, total precipitation, road density, construction area proportions, and gross domestic product (GDP) were positively correlated with foodborne diseases. GWLR results indicated that the strength and significance of these relationships varied locally, and the predicted risk map revealed that the risk of foodborne diseases caused by Vibrio parahaemolyticus was higher in urban areas (60.6%) than rural areas (20.1%). Finally, distance from the coastline was negatively correlated with predicted regional risks. This study provides a spatial perspective for the relevant departments to prevent and control foodborne diseases.