The rapid increase of urban contaminated sites along China's urbanization during the last 30 years

Distribution, assessment, and causality analysis of soil heavy metals pollution in complex contaminated sites: a case study of a chemical plant

To effectively prevent and control pollution from heavy metals (HMs) in urban soils, it is essential to thoroughly understand the contamination status of contaminated sites. In this study, the contamination status and sources of six HMs (As, Cu, Cr, Ni, Pb, Cd) in the soil of a decommissioned chemical plant in southern China were comprehensively analyzed. The results indicated that the average concentration of HMs followed the sequence: Cr > Pb > Cu > Ni > As > Cd. Heavy metal accumulation in the upper soil layer was predominantly observed in industrial zones and low-lying areas, with notable variations in concentration along the vertical profile. Certain sections of the site exhibited severe HM contamination, particularly with Cu levels exceeding the background value by 46.77 times. Cd presented significant ecological risks in specific areas, with an average Ecological Index of 96.09. Carcinogenic and non-carcinogenic risks were identified at three and six sampling points, respectively, with sampling point S103 demonstrating both types of risks. The causes of HM contamination were primarily attributed to anthropogenic activities. Horizontal dispersion was mainly influenced by production operations and topographical features, while vertical distribution was predominantly affected by the permeability characteristics of the strata. The causality analysis incorporating production activities and topographical factors provides novel perspectives for understanding sources of contamination at contaminated sites. The study outcomes can offer guidance for the assessment and surveying of urban industrial pollution sites.

Unveiling the overlooked threat: antibiotic resistance in groundwater near an abandoned sulfuric acid plant in Xingyang, China

Groundwater near a sulfuric acid plant in Xingyang, Henan, China was sampled from seven distinct sites to explore the prevalence of antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs). Results showed that genes aadA, blaCTX-M, tetA, qnrA, and sul1 were detected with 100% frequency followed by aac(6')-Ib (85.71%), ermB (85.71%), and tetX (71.42%). Most abundant ARGs were sul1 in LSA2 (1.15 × 1011 copies/mL), tetA in LSA6 (4.95 × 1010 copies/mL), aadA in LSA2 (4.56 × 109 copies/mL), blaCTX-M in LSA4 (1.19 × 109 copies/mL), and ermB in LSA5 (1.07 × 109 copies/mL). Moreover, in LSA2, intl1 as a marker of class 1 integron emerged as the most abundant gene as part of MGE (2.25 × 1011 copies/mL), trailed by ISCR1 (1.57 × 109 copies/mL). Environmental factors explained 81.34% of ARG variations, with a strong positive correlation between the intl2 and blaCTX-M genes, as well as the ISCR1 gene and qnrA, tetA, intl2, and blaCTX-M. Furthermore, the intI1 gene had a strong positive connection with the aadA, tetA, and sul1 genes. Moreover, the aac(6')-Ib gene was associated with As, Pb, Mg, Ca, and HCO3-. The intl2 gene was also shown to be strongly associated with Cd. Notably, network analysis highlighted blaCTX-M as the most frequently appearing gene across networks of at least five genera. Particularly, Lactobacillus, Plesiomonas, and Ligilactobacillus demonstrated correlations with aadA, qnrA, blaCTX-M, intI2, and ISCR1. Based on 16S rRNA sequencing, the dominant phyla were Proteobacteria, Firmicutes, Bacteroidota, Acidobacteriota, and Actinobacteriota, with dominant genera including Pseudomonas, Ligilactobacillus, Azoarcus, Vogesella, Streptococcus, Plesiomonas, and Ferritrophicum. These findings enhance our understanding of ARG distribution in groundwater, signaling substantial contamination by ARGs and potential risks to public health.

Machine learning coupled with causal inference to identify COVID-19 related chemicals that pose a high concern to drinking water

Various synthetic substances were utilized in large quantities during the recent coronavirus pandemic, COVID-19. Some of these chemicals could potentially enter drinking water sources. Persistent, mobile, and toxic (PMT) substances have been recognized as a threat to drinking water resources. It has not yet been assessed how many COVID-19 related substances could be considered PMT substances. One reason is the lack of high-quality experimental data for the identification of PMT substances. To solve this problem, we applied a machine learning model to identify the PMT substances among COVID-19 related chemicals. The optimal model achieved an accuracy of 90.6% based on external test data. The model interpretation and causal inference indicated that our approach understood causation between PMT properties and molecular descriptors. Notably, the screening results showed that over 60% of the COVID-19 chemicals considered are candidate PMT substances, which should be prioritized to prevent undue pollution of water resources.

Development of a Multifaceted Perspective for Systematic Analysis, Assessment, and Performance for Environmental Standards of Contaminated Sites

Contaminated soil and groundwater can pose significant risks to human health and ecological environments, making the remediation of contaminated sites a pressing and sustained challenge. It is significant to identify key performance indicators and advance environmental management standards of contaminated sites. The traditional study currently focuses on the inflexible collection of related files and displays configurable limitations regarding integrated assessment and in-depth analysis of published standards. In addition, there is a relative lack of research focusing on the analysis of different types of standard documents. Herein, we introduce a cross-systematic retrospective and review for the development of standards of the contaminated sites, including the comprehensive framework, multifaceted analysis, and improved suggestion of soil and groundwater standards related to the environment. The classification and structural characteristics of different types of files are systematically analyzed of over 300 national, trade, local, and group standards for the contaminated sites. It exhibits that trade standards are the main types and testing methods are the important format within numerical considerations of soil standards. The guide standard serves as a crucial component in environmental management for investigating, assessing, and remediating of contaminated sites. Future improvement plans and development directions are proposed for advancing robust technical support for effective soil contamination prevention and control. This multidimensional analysis and the accompanying suggestions can provide improved guidance for Chinese environmental management of contaminated sites and sparkle the application of standards in a wide range of countries.