Today, indoor air pollution is a major concern. So far, many quantitative and qualitative studies have been conducted on particulate matter pollution in closed environments, but not much research has been done to measure air pollution in subway station. In this study, we have investigated the concentrations of PM10, PM2.5 and TSP particles in 12 underground stations on the oldest and main Tehran metro line, in two seasons, autumn and spring. For sampling suspended particles, we have used a portable direct reading device for monitoring suspended-particles (HAZDUST EPMA5000). We also used Pair T- test to compare the particle concentrations in different modes of the ventilation system (on, off, and inlet air) and Three-way variance analyze. According to the results, the mean concentrations of PM2.5-PM10 - TSP values in line-1 on the station platforms are significantly higher in spring than in autumn, off state of the ventilation system than on state of the ventilation system (P <0.001). Also, the concentration of particles measured in the air of subway stations is higher in the off state of ventilation systems, compared to Inlet air to stations (P<0.001). There is a correlation between concentration of particles measured in different sampling season, condition of the ventilation mode (on, off, inlet air) (P<0.001). Improving the efficiency of ventilation systems (equipped with a suitable filter) and fan in stations is suggested as one of the factors to reduce the concentration of particles, especially in spring.

In developing countries, where high levels of antimicrobial resistance are observed in hospitals, the surveillance of this phenomenon in wastewater treatment plants (WWTPs) and the environment is very limited, especially using cutting-edge culture-independent methods. In this study, the composition of bacterial communities, the resistome and mobilome (the pool of antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs), respectively) at a WWTP were determined using shotgun metagenomics and culture-based approaches. Wastewater samples were collected at four sampling points of a WWTP in Antioquia, Colombia. A total of 24 metagenomes were analyzed. Specifically, there were marked differences in bacterial community composition, resistome, and mobilome, according to the WWTP sampling points. Bacterial families of clinical importance such as Moraxellaceae, Aeromonadaceae, and Enterobacteriaceae were mainly detected in the WWTP influent and effluent samples. Genes encoding resistance to macrolide-lincosamide-streptogramin, β-lactams, and those conferring multidrug resistance (e.g., acrB, adeG, and mexD) were the most abundant. Moreover, some clinically important ARGs such as blaKPC₋₂ and blaCTX₋M, and others not reported locally, such as blaTEM₋₁₉₆, blaGES₋₂₃, blaOXA₋₁₀, mcr-3, and mcr-5 were frequently detected. Co-occurrence network analyses indicated a significant association of ARGs such as blaOXA₋₅₈ and blaKPC genes with Aeromonadaceae and Enterobacteriaceae. Among the markers of MGEs, intI1 and ISCR8 were the most frequently detected. Altogether, this work reveals the importance of shotgun metagenomics and culture-based approaches in antimicrobial resistance studies. The findings also support that WWTPs are hotspots for antimicrobial resistance, whose analysis constitutes a powerful tool to predict the impact of antimicrobial resistance in a population.

Shipping makes up the major proportion of global transportation and results in an increasing emission of air pollutants. It accounts for 3.1%, 13%, and 15% of the annual global emissions of CO₂, SOₓ, and NOₓ, respectively. Hence, effective regulatory measures in line with the International Maritime Organization requirements regarding the fuel sulfur content (FSC) used in emission control areas are essential. An imaging detection approach is proposed to estimate SO₂, CO₂, and NO concentrations of exhaust gas and then calculate FSC based on the estimated gas concentrations. A multi-task deep neural network was used to extract the features from the ultraviolet and thermal infrared images of the exhaust plume. The network was trained to predict various gas concentrations. The results show high prediction accuracy for the remote monitoring of ship emissions.

Air pollution in the form of fine particulate matter (PM₂.₅) has been linked to adverse respiratory outcomes in children. However, the magnitude of this association in South Asia and sources of PM₂.₅ that drive adverse health effects are largely unknown. This study evaluates associations between short-term variation in ambient PM₂.₅ and incidence of pneumonia and upper respiratory infections among children in Dhaka, Bangladesh. We also perform an exploratory analysis of the PM₂.₅ source composition that is most strongly associated with health endpoints. We leveraged data from health surveillance of children less than five years of age between 2005 and 2014 in Kamalapur, Bangladesh, including daily physician-confirmed diagnoses of pneumonia and upper respiratory infection. Twice-weekly source-apportioned ambient PM₂.₅ measurements were obtained for the same period, and Poisson regression adjusted for time-varying covariates was used to estimate lagged associations between ambient PM₂.₅ and respiratory infection. We use complementary matching and stratification approaches to evaluate whether these associations vary across PM₂.₅ source composition. Total PM₂.₅ mass was associated with a modest increase in incidence of pneumonia, with a peak effect size two days after exposure (rate ratio = 1.032; 95% confidence interval = 1.008–1.056). We did not identify a significant association between PM₂.₅ and upper respiratory infection. Stratified and matching analyses suggested this association was stronger among days when ambient PM₂.₅ had a higher mass percent associated with brick kiln and fugitive lead emissions.: This study suggests that elevated ambient PM₂.₅ contributes to increased incidence of child pneumonia in urban Dhaka, and that this relationship varies among days with different source composition of PM₂.₅.

Since 2004, there has been a marked diversification in the methods used to determine aquatic microplastic (MP) concentrations. Despite calls for a unified approach to MP sampling, the proliferation of new methods has accelerated in recent years. Both minor method adaptations and entirely novel approaches have been introduced to overcome barriers to reliable MP sampling, extraction and quantification, resulting in a variety of complimentary but also competing approaches. However, there is little clarity regarding the extent to which new methods are acknowledged and adopted, or of the apparent drivers of, as well as barriers to, said adoption. To explore these issues, the rate of method diversification was examined in a systematic review. The rate and degree of diversification were determined by scoring each method by its “degree of novelty”: highly novel methods, secondary adaptations of existing methods and smaller, tertiary adaptations of existing methods. This analysis revealed that the rate of method diversification has been greatest since 2011. Our results indicate limited use of these novel methods and adaptations in the subsequent literature, with many researchers falling back on methods that are well established in the existing literature. Importantly, there is little consistency in the units used when reporting MP concentrations. However, these differences are seldom driven by method selection and are rather the result of discrepancies between researchers. Thus, in understanding the requirements of comparability and consistent reporting for monitoring purposes, we can apply a diverse approach to sampling whilst maintaining the applicability and usefulness of the resulting data.

COVID-19 pandemic has led to concerns on the circulation of SARS-CoV-2 in the environment, its infectivity from the environment and, the relevance of transmission via environmental compartments. During 31 weeks, water samples were collected from a heavily contaminated stream going through an urban, underprivileged community without sewage collection. Our results showed a statistically significant correlation between cases of COVID-19 and SARS in the community, and SARS-CoV-2 concentrations in the water. Based on the model, if the concentrations of SARS-CoV-RNA (N1 and N2 target regions) increase 10 times, there is an expected increase of 104% [95%CI: (62–157%)] and 92% [95%CI: (51–143%)], respectively, in the number of cases of COVID-19 and SARS. We believe that differences in concentration of the virus in the environment reflect the epidemiological status in the community, which may be important information for surveillance and controlling dissemination in areas with vulnerable populations and poor sanitation. None of the samples were found infectious based cultures. Our results may be applicable globally as similar communities exist worldwide.

In soils, the presence of clinically relevant bacteria carrying ARGs, including extended-spectrum β-lactamase- and plasmid-mediated AmpC β-lactamase-encoding genes, is an underestimated public health problem that requires more attention. For this investigation, 300 samples from agricultural and non-agricultural soils were used to obtain 41 MDR E. coli isolates, standing out the resistance to β-lactams, fluoroquinolones and colistin. Virulence genes related to diarrheagenic E. coli and extraintestinal pathogenic E. coli were detected. Several ARGs were found, highlighting the presence of at least one β-lactamase-encoding gene (blaTEM, blaCMY, blaSHV, blaOXA₋₁₋ₗᵢₖₑ, blaCTX₋M₋₂, and/or blaCTX₋M₋₁₅) in each isolate. Among the fluoroquinolone-resistant E. coli isolates, the plasmid-mediated quinolone resistance genes (qnrB and oqxA) and substitutions in the quinolone resistance-determining regions were detected. Some isolates were resistant to colistin (MICs of 4–8 mg/L) and, although no mcr-like gene was detected, substitutions in the two-component systems involving PhoP/PhoQ and PmrA/PmrB were found. Furthermore, the E. coli isolates presented plasmids and class 1 integrons, the last one detected in all isolates. The ARGs blaTEM, aadA and dfrA and the lpfA virulence-associated gene presented statistically significant differences (P < 0.05) in agricultural soils, while the blaOXA₋₁₋ₗᵢₖₑ gene presented a statistically significant difference in non-agricultural soils. Thirty-eight sequence types (STs) were identified among the isolates, spotlighting the 20 different STs that carried blaCMY and blaCTX₋M₋ₜyₚₑ genes and those commonly reported in infections worldwide. The occurrence of virulent, multidrug- and colistin-resistant E. coli isolates in soils could lead to contamination of surrounding environments and food, increasing the risk of human and animal exposure. Therefore, this study contributes to a better understanding of E. coli in soils and reinforces the importance of the One Health approach to antimicrobial resistance surveillance.

As zoned areas of industries, industrial parks have great impacts on the environment. Several studies have demonstrated that chemical compounds and heavy metals released from industrial parks can contaminate soil, water, and air. However, as an emerging pollutant, antimicrobial resistance genes (ARGs) in industrial parks have not yet been investigated. Here, we collected soil samples from 35 sites in an industrial park in China and applied a metagenomics strategy to profile the ARGs and virulence factors (VFs). We further compared the relative abundance of ARGs between the sites (TZ_31–35) located in a beta-lactam antimicrobial-producing factory and other sites (TZ_1–30) in this industrial park. Metagenomic sequencing and assembly generated 14, 383, 065 contigs and 17, 631, 051 open reading frames (ORFs). Taxonomy annotation revealed Proteobacteria and Actinobacteria as the most abundant phylum and class, respectively. The 32 pathogenic bacterial genera listed in the virulence factor database (VFDB) were all identified from the soil metagenomes in this industrial park. In total, 685,354 ARGs (3.89% of the ORFs) and 272,694 virulence factors (VFs) (1.55% of the ORFs) were annotated. These ARGs exhibited resistance to several critically important antimicrobials, such as rifampins, fluroquinolones, and beta-lactams. In addition, no significant difference in the relative abundance of ARGs was observed between sites TZ_31–35 and TZ_1–30, indicating that ARGs have already disseminated widely in this industrial park. The present study gave us a better understanding of the whole picture of the resistome and virulome in the soil of the industrial park and suggested that we should treat the industrial park as a whole in the surveillance and maintenance of ARGs.

Due to their extensive use in both agricultural and non-agricultural applications, pesticides are a major source of environmental contamination. Honey bee colonies are proven sentinels of these and other contaminants, as they come into contact with them during their foraging activities. However, active sampling strategies involve a negative impact on these organisms and, in most cases, the need of analyzing multiple heterogeneous matrices. Conversely, the APIStrip-based passive sampling is innocuous for the bees and allows for long-term monitorings using the same colony. The versatility of the sorbent Tenax, included in the APIStrip composition, ensures that comprehensive information regarding the contaminants inside the beehive will be obtained in one single matrix. In the present study, 180 APIStrips were placed in nine apiaries distributed in Denmark throughout a six-month sampling period (10 subsequent samplings, April to September 2020). Seventy-five pesticide residues were detected (out of a 428-pesticide scope), boscalid and azoxystrobin being the most frequently detected compounds. There were significant variations in the findings of the sampling sites in terms of number of detections, pesticide diversity and average concentration. A relative indicator of the potential risk of pesticide exposure for the honey bees was calculated for each sampling site. The evolution of pesticide detections over the sampling periods, as well as the individual tendencies of selected pesticides, is herein described. The findings of this large-scale monitoring were compared to the ones obtained in a previous Danish, APIStrip-based pilot monitoring program in 2019. Samples of honey and wax were also analyzed and compared to the APIStrip findings.