Quantitative cancer risk assessment and local mortality burden for ambient air pollution in an eastern Mediterranean City

Health risks posed by ambient air pollutants to the urban Lebanese population have not been well characterized. The aim of this study is to assess cancer risk and mortality burden of non-methane hydrocarbons (NMHCs) and particulates (PM) based on two field-sampling campaigns conducted during summer and winter seasons in Beirut. Seventy NMHCs were analyzed by TD-GC-FID. PM_2.5 elemental carbon (EC) components were examined using a Lab OC-EC aerosol Analyzer, and polycyclic aromatic hydrocarbons were analyzed by GC-MS. The US EPA fraction-based approach was used to assess non-cancer hazard and cancer risk for the hydrocarbon mixture, and the UK Committee on Medical Effects of Air Pollutants (COMEAP) guidelines were followed to determine the PM_2.5 attributable mortality burden. The average cumulative cancer risk exceeded the US EPA acceptable level (10⁻⁶) by 40-fold in the summer and 30-fold in the winter. Benzene was found to be the highest contributor to cancer risk (39–43%), followed by 1,3-butadiene (25–29%), both originating from traffic gasoline evaporation and combustion. The EC attributable average mortality fraction was 7.8–10%, while the average attributable number of deaths (AD) and years of life lost (YLL) were found to be 257–327 and 3086–3923, respectively. Our findings provide a baseline for future air monitoring programs, and for interventions aiming at reducing cancer risk in this population.

     

Exposure assessment of endocrine disruptors in bottled drinking water of Lebanon

Bisphenol A (BPA) is a commonly used monomer in various products including bottled water. Numerous studies have reported endocrine adverse effects and neoplasia associated with BPA exposure in animals. However, considerable discrepancies exist among these studies with respect to both the nature of the toxic effects and the threshold dose. In Lebanon, 19-L polycarbonate (PC) bottles of drinking water are widely used in urban areas. The present study aims at assessing BPA human exposure and associated health risks from drinking water in Lebanese. A total of 22 bottled water sources, packaged in PC, were identified from licensed and non-licensed sources. Water samples were analyzed following exposure to sunlight for 72 h. BPA in water was quantified by HPLC, and other potential organic pollutants were screened by GC/MS. Fifty-nine percent of samples showed BPA levels above detection limits (>0.05 ng/mL). The median BPA level was 0.1 ng/mL (range 0.05 to 1.37 ng/mL). The mean BPA level for the total number of samples was 0.169 ng/mL (±0.280). A higher mean BPA level was found in water from licensed companies compared to non-licensed sources, however, not statistically significant. Screening showed the presence of dibutyl-phthalate and dioctyl-phthalate in only two samples. Endocrine disruptors (EDR) are ubiquitous contaminants in bottled water in Lebanon with potential health risk implications. Although estimated exposure levels are below the reference dose (RfD), further studies are needed to quantitate exposure from various sources and to investigate EDR contribution to existing epidemics in the country.     

Feature selection approaches for predictive modelling of cadmium sources and pollution levels in water springs

The World Health Organization lists cadmium (Cd) as one of the top ten chemicals of public health concern. Cd is toxic at relatively low exposure levels and has acute and chronic effects on both health and the environment. In this study, we investigate a suite of data-driven methods that could assist decision-makers in estimating Cd levels in water springs, and in identifying polluting sources. Machine learning (ML) regression models were used to identify sources of contamination and predict Cd levels based on support vector machines and a variety of tree-based models, including Random Forests, M5Tree, CatBoost, and gradient boosting. Feature selection analysis revealed that heavy traffic and distance to a major power plant in the sampled area play a leading role in springs Cd contamination, together with precipitation levels and average of slopes of the closest waste dumps upstream to sampled springs. Our best performing ML model was the Adaboost regression tree using all the features (RMSE = 19.36, R^2 = 0.64). Our findings highlight the effectiveness of predictive data-driven modeling in addressing environmental challenges, particularly in high-risk areas with low resources.