Application of machine learning for antibiotic resistance in water and wastewater: A systematic review.

Antibiotic resistance (AR) is considered one of the greatest global threats in the current century, which can only be overcome if all interconnected areas of humans, animals and the environment are taken into account as part of the One Health concept proposed by the World Health Organization (WHO). Water and wastewater are among the most important environmental media of AR sources, where the phenomena are generally non-linear. Therefore, the aim of this study was to investigate the application of machine learning-based methods (MLMs) to solve AR-induced problems in water and wastewater. For this purpose, most relevant databases were searched in the period between 1987 and 2023 to systematically analyze and categorize the applications. Accordingly, the results showed that out of 12 applications, 11 (91.6%) were for shallow learning and 1 (8.3%) for deep learning. In shallow learning category, n = 6, 50% of the applications were regression and n = 4, 33.3% were classification, mainly using artificial neural networks, decision trees and Bayesian methods for the following objectives: Predicting the survival of antibiotic-resistant bacteria (ARB), determining the order of influencing parameters on AR-based scores, and identifying the major sources of antibiotic resistance genes (ARGs). In addition, only one study (8.3%) was found for clustering and no study for association. Surprisingly, deep learning had been used in only one study (8.3%) to predict ARGs sequences. Therefore, working on the knowledge gaps of AR, especially using clustering, association and deep learning methods, would be a promising option to analyze more aspects of the related problems. However, there is still a long way to go to consider and apply MLMs as unique approaches to study different aspects of AR in water and wastewater.

Short-term effect of multi-pollutant air quality indexes and PM2.5 on cardiovascular hospitalization in Hamadan, Iran: a time-series analysis.

Air pollutants are the most important environmental factors that contributed to cardiovascular disease (CVD). The present study aimed to investigate the number of hospitalization due to heart failure (HF) and myocardial infarction (MI) following the air pollutant exposure using a time-series regression analysis with a distributed lag model in Hamadan, Iran (2015-2019). A total of 2091 cases of CVD were registered. Based on the findings, the highest health effects on HF hospitalization were observed with air quality health index (AQHI) at lag 9 (RR = 1.043, 95% CI 0.991-1.098), and air quality index (AQI) at lags 2, 7, and 9 (RR = 1.001, 95% CI 0.998-1.002), for an increase in 1 unit of the indexes, and with PM2.5 at lag 0 (RR = 1.001, 95% CI 0.996-1.004) for 10 µg/m3 increase in PM2.5 levels. The highest health effects on MI hospitalization were calculated with AQHI at lag 10 (RR = 1.059, 95% CI 1.001-1.121) and AQI at lags 1 and 2 (RR = 1.001, 95% CI 0.998-1.002), for an increase in 1 unit of the indexes, and with PM2.5 at lag 8 (RR = 1.002, 95% CI 0.997-1.005) for 10 µg/m3 increase in PM2.5 levels. According to a seasonal classification, results showed that hospitalization in the warm season was higher than that of the cold season. Based on our knowledge, the current study is the first study that investigated the effect of air quality indexes on hospitalization due to HF and MI in Iran. Findings can provide basic information to plan preventive measures for reducing exposure chance and hospitalization rate in high-risk people.

Phase distribution and risk assessment of PAHs in ambient air of Hamadan, Iran.

In the present study, both gaseous and particulate (PM with dae <2.5 µm) phases of polycyclic aromatic hydrocarbons (PAHs) were measured in the ambient air of Hamadan city, Iran. For this reason, two low-volume samplers equipped with glass fiber filters were used for sampling of particulate phase (N = 30) and XAD-2 sorbent tubes were applied for sampling gaseous phase of PAHs (N = 30). The sampling was conducted during warm and cold seasons in 2019. The average of cold/warm season ratios for Σ16PAH and PM concentrations were 1.14 and 0.62, respectively. Summed PAHs concentration were determined to be in the range 0.008-59.46 (mean: 11.61) ng/m3 and 0.05-40.83 (mean: 10.22) ng/m3 for the cold and warm seasons, respectively. A negative Pearson correlation coefficient was obtained for wind speed and relative humidity. The average Benzo (a) Pyrene equivalent carcinogenic (BaPeq) levels in the cold season were lower than the maximum permissible risk level of 1 ng/m3 for BaP. The BaP toxicity equivalency (ΣBaPTEQ) and BaP mutagenicity equivalency (ΣBaPMEQ) appeared to be significantly higher in the cold season (averaging 0.35 and 1.65 ng/m3, respectively) than those in warm season. Health risk assessment was performed for children and adults based on BaPeq, inhalation cancer risk. The diagnostic ratios of individual PAHs concentration showed that the significant sources of PAH emissions may be related to light duty vehicles (LDVs) in Hamadan. Although, some other sources such as pyrogenic source and petrol combustion were also suggested.

The Assessment of Trihalomethanes Concentrations in Drinking Water of Hamadan and Tuyserkan Cities, Western Iran and Its Health Risk on the Exposed Population.

BACKGROUND: The aim of the present study was to calculate and to assess the potential lifetime cancer risks for trihalomethanes from consuming chlorinated drinking water in Hamadan and Tuyserkan cities, western Iran in 2016-2017. STUDY DESIGN: A cross-sectional study. METHODS: Seventy-two water samples were collected from the distribution systems and from the outlet of water treatment plants (WTPs) and the experiments were carried out to determine the desired parameters. All the sampling and measurement methods were according to Standard Methods. The obtained data were analyzed using SPSS software. RESULTS: The mean concentration of total THMs in the summer and winter was 42.75 and 17.75 µg/L, respectively, below the WHO and Iranian standard. The positive correlation was observed between temperature and THMs levels. Moreover, THMs concentration in Shahid Beheshti's WTP was several times lower than in Ekbatan's WTP. Chloroform, the dominant species of THMs, was identified at different sampling points. The highest cancer risk in Hamadan was 1.4×10-5 and 4.8×10-5 for male and female, respectively; and the cancer risk was obtained to be 5.6×10-7-2.26×10-6 in Tuyserkan. CONCLUSION: The drinking water obtained from the studied area is safe in terms of THMs concentration. Nevertheless, the highest cancer risk was higher than the EPA's acceptable level of 10-6.

Ultrasound-assisted sorption of Pb(ii) on multi-walled carbon nanotube in presence of natural organic matter: an insight into main and interaction effects using modelling approaches of RSM and BRT.

In real-scale applications, where NPs are injected into the aqueous environment for remediation, they may interact with natural organic matter (NOM). This interaction can alter nanoparticles' (NPs) physicochemical properties, sorption behavior, and even ecological effects. This study aimed to investigate sorption of Pb(ii) onto multi-walled carbon nanotube (MWCNT) in presence of NOM. The predominant behavior of the process was examined comparatively using response surface methodology (RSM) and boosted regression tree (BRT)-based models. The influence of four main effective parameters, namely Pb(ii) and humic acid (HA) concentrations (mg L-1), pH, and time (min) on Pb removal (%) was evaluated by contributing factor importance rankings (BRT) and analysis of variance (RSM). The applicability of the BRT and RSM models for description of the predominant behavior in the design space was checked and compared using statistics of absolute average deviation (AAD), mean absolute error (MAE), root mean square error (RMSE), and multiple correlation coefficient (R 2). The results showed that although both approaches exhibited good performance, the BRT model was more precise, indicating that it could be a powerful method for the modeling of NOM-presence studies. Importance rankings of BRT displayed that the effectiveness order of the studied parameters is pH > time > Pb(ii) concentration > HA concentration. Although HA concentration showed the least effect in comparison with three other studied parameters theoretically, the experimental results revealed that Pb(ii) removal is enhanced in presence of HA (73% vs. 81.77%), which was confirmed by SEM/EDX analyses. Hence, maximum removal (R% = 81.77) was attained at an initial Pb(ii) concentration of 9.91 mg L-1, HA concentration of 0.3 mg L-1, pH of 4.9, and time of 55.2 min.