Spatial analysis of chromium in southwestern part of Iran: probabilistic health risk and multivariate global sensitivity analysis

Monte Carlo-based probabilistic risk assessment for cement workers exposed to heavy metals in cement dust

This study assessed the carcinogenic and non-carcinogenic health risks of cement plant workers exposed to chromium (Cr), arsenic (As), cadmium (Cd), and lead (Pb) in cement dust using a probabilistic approach. Air samples were collected according to NIOSH 7900 and OSHA ID-121 methods and analyzed by an graphite furnace atomic absorption spectrometer. The EPA inhalation risk assessment model and Monte Carlo simulation were utilized to assess the health risks. Sensitivity analysis was used to determine the influencing parameters on health risk. The average concentrations of As and Pb exceeded the occupational exposure limit (OEL), reaching a maximum of 3.4 and 1.7 times the OEL, respectively, in the cement mill. Individual metals' cancer risk exceeded the 1E-4 threshold in ascending order of Cd < As < Cr. The mean cancer risk of Cr ranged from 835E-4 (in raw mill) to 2870E-4 (in pre-heater and kiln). Except for Cd, the non-cancer risk of metals exceeded the standard (hazard index, HQ = 1) in the ascending order of Pb < As < Cr. The mean HQ of Cr ranged from 162.13 (in raw mill) to 558.73 (in pre-heater and kiln). After adjusting for control factors, the cancer and non-cancer risks remained over the respective recommended levels. Sensitivity analysis revealed that the concentration of Cr was the most influential parameter on both carcinogenic (78.5%) and non-carcinogenic (88.06%) risks. To protect the health of cement factory employees, it is recommended to minimize cement dust emissions, implement job rotation, and use raw materials with low levels of heavy metals.

Investigating sources, driving forces and potential health risks of nitrate and fluoride in groundwater of a typical alluvial fan plain

Groundwater of alluvial fan plains is the foremost water source, especially in arid/semiarid regions. Its contaminants are big issues for water supply and public health concern. To reveal the groundwater chemistry, contaminants sources and health threats in alluvial aquifers, 81 groundwaters were collected from a typical alluvial fan plain of northern China for nitrogen, fluoride and major ions analysis. Statistical analysis and hydrochemical diagrams as well as human health risk assessment were performed. Nitrate is widely distributed and 53% of groundwaters exceed the permissible limit with the maximum concentration up to 326 mg/L. The distributions of nitrite, ammonia and fluoride contaminants are sporadic in spatial, and the concentrations of fluoride in groundwaters are slightly beyond the permissible limit of 1 mg/L. The hydrochemical facies shift from HCO₃-Ca or Mixed HCO₃-Na·Ca type to Mixed Cl-Mg·Ca and ClCa type with the increase of nitrate content. Two factors (Factor-1 and Factor-2) are extracted by factor analysis and account 63% of the total variances. The positive loading of F^- and negative loading of NO₃^- on Factor-2 reveal geogenic and anthropogenic origins, respectively. The significant positive loadings of TDS, TH, SO₄^2-, Cl^-, Ca²⁺, Mg²⁺ on Factor-1 reveal the governing mechanisms on groundwater chemistry by intermixed sources of geogenic origins and anthropogenic inputs. Hydrogeochemical evolution in the study area is driven by both water-rock interaction and anthropogenic forces. Anthropogenic inputs/influences are the dominated forces increasing groundwater nitrate content and salinity in the piedmont zone and the residential and industrial zone of the southeastern lower parts, and would pose potential non-carcinogenic risks to various populations via oral intake pathway. Rational measures should be taken to protect groundwater quality out of the threats of anthropogenic pollution. The geogenic fluoride in groundwater would threat the health of children through oral pathway and should be also concerned. CAPSULE: The driving forces of groundwater chemistry in alluvial fan plains were revealed using integrated approach of factor analysis and geostatistical modelling.