Impacts of socioeconomic and environmental factors on neoplasms incidence rates using machine learning and GIS: a cross-sectional study in Iran.

Neoplasm is an umbrella term used to describe either benign or malignant conditions. The correlations between socioeconomic and environmental factors and the occurrence of new-onset of neoplasms have already been demonstrated in a body of research. Nevertheless, few studies have specifically dealt with the nature of relationship, significance of risk factors, and geographic variation of them, particularly in low- and middle-income communities. This study, thus, set out to (1) analyze spatiotemporal variations of the age-adjusted incidence rate (AAIR) of neoplasms in Iran throughout five time periods, (2) investigate relationships between a collection of environmental and socioeconomic indicators and the AAIR of neoplasms all over the country, and (3) evaluate geographical alterations in their relative importance. Our cross-sectional study design was based on county-level data from 2010 to 2020. AAIR of neoplasms data was acquired from the Institute for Health Metrics and Evaluation (IHME). HotSpot analyses and Anselin Local Moran's I indices were deployed to precisely identify AAIR of neoplasms high- and low-risk clusters. Multi-scale geographically weight regression (MGWR) analysis was worked out to evaluate the association between each explanatory variable and the AAIR of neoplasms. Utilizing random forests (RF), we also examined the relationships between environmental (e.g., UV index and PM2.5 concentration) and socioeconomic (e.g., Gini coefficient and literacy rate) factors and AAIR of neoplasms. AAIR of neoplasms displayed a significant increasing trend over the study period. According to the MGWR, the only factor that significantly varied spatially and was associated with the AAIR of neoplasms in Iran was the UV index. A good accuracy RF model was confirmed for both training and testing data with correlation coefficients R2 greater than 0.91 and 0.92, respectively. UV index and Gini coefficient ranked the highest variables in the prediction of AAIR of neoplasms, based on the relative influence of each variable. More research using machine learning approaches taking the advantages of considering all possible determinants is required to assess health strategies outcomes and properly formulate policy planning.

Occurrence of per- and polyfluoroalkyl substances in aquatic environments and their removal by advanced oxidation processes.

Per- and polyfluoroalkyl substances (PFAS), one of the main categories of emerging contaminants, are a family of fluorinated organic compounds of anthropogenic origin. PFAS can endanger the environment and human health because of their wide application in industries, long-term persistence, unique properties, and bioaccumulation potential. This study sought to explain the accumulation of different PFAS in water bodies. In aquatic environments, PFAS concentrations range extensively from <0.03 (groundwater; Melbourne, Australia) to 51,000 ng/L (Groundwater, Sweden). Additionally, bioaccumulation of PFAS in fish and water biota has been stated to range from 0.2 (Burbot, Lake Vättern, Sweden) to 13,900 ng/g (Bluegill samples, U.S.). Recently, studies have focused on PFAS removal from aqueous solutions; one promising technique is advanced oxidation processes (AOPs), including microwaves, ultrasound, ozonation, photocatalysis, UV, electrochemical oxidation, the Fenton process, and hydrogen peroxide-based and sulfate radical-based systems. The removal efficiency of PFAS ranges from 3% (for MW) to 100% for UV/sulfate radical as a hybrid reactor. Therefore, a hybrid reactor can be used to efficiently degrade and remove PFAS. Developing novel, efficient, cost-effective, and sustainable AOPs for PFAS degradation in water treatment systems is a critical area of research.