Pollutants

For about the past eight decades, high concentrations of naturally occurring fluoride have been detected in groundwater in different parts of India. The chronic consumption of fluoride in high concentrations is recognized to cause dental and skeletal fluorosis. We have used the random forest machine-learning algorithm to model a data set of 12 600 groundwater fluoride concentrations from throughout India along with spatially continuous predictor variables of predominantly geology, climate, and soil parameters. Despite only surface parameters being available to describe a subsurface phenomenon, this has produced a highly accurate prediction map of fluoride concentrations exceeding 1.5 mg/L at 1 km resolution throughout the country. The most affected areas are the northwestern states/territories of Delhi, Gujarat, Haryana, Punjab, and Rajasthan and the southern states of Andhra Pradesh, Karnataka, Tamil Nadu, and Telangana. The total number of people at risk of fluorosis due to fluoride in groundwater is predicted to be around 120 million, or 9% of the population. This number is based on rural populations and accounts for average rates of groundwater consumption from nonmanaged sources. The new fluoride hazard and risk maps can be used by authorities in conjunction with detailed groundwater utilization information to prioritize areas in need of mitigation measures.

Compound-specific isotope analysis (CSIA) of polar organic micropollutants in environmental waters requires a processing of large sample volumes to obtain the required analyte masses for analysis by gas chromatography/isotope-ratio mass spectrometry (GC/IRMS). However, the accumulation of organic matter of unknown isotopic composition in standard enrichment procedures currently compromises the accurate determination of isotope ratios. We explored the use of molecularly imprinted polymers (MIPs) for selective analyte enrichment for ¹³C/¹²C and ¹⁵N/¹⁴N ratio measurements by GC/IRMS using 1H-benzotriazole, a typical corrosion inhibitor in dishwashing detergents, as example of a widely detected polar organic micropollutant. We developed procedures for the treatment of > 10 L of water samples, in which custom-made MIPs enabled the selective cleanup of enriched analytes in organic solvents obtained through conventional solid-phase extractions. Hydrogen bonding interactions between the triazole moiety of 1H-benzotriazole, and the MIP were responsible for selective interactions through an assessment of interaction enthalpies and ¹⁵N isotope effects. The procedure was applied successfully without causing isotope fractionation to river water samples, as well as in- and effluents of wastewater treatment plants containing μg/L concentrations of 1H-benzotriazole and dissolved organic carbon (DOC) loads of up to 28 mg C/L. MIP-based treatments offer new perspectives for CSIA of organic micropollutants through the reduction of the DOC-to-micropollutant ratios.

Considerable pollutant loads can enter surface waters during rain events. Three factors challenge quantification of these pollutant fluxes using traditional sampling methods: (i) concentration fluctuations; (ii) unknown event duration; and (iii) placement, operation, and maintenance of equipment. Passive samplers offer the advantage of sampling in a continuous mode without power supply. However, variable uptake rates due to environmental factors and desorption in the case of fluctuating concentrations can affect the accuracy of time-weighted average (TWA) concentration estimates. While uncertainties related to environmental factors could be accounted for with additional effort, we can neither control nor quantify the concentration variability. We present measured and modelled concentration profiles at high temporal resolution and provide a systematic approach to assessing deviations from true TWA concentration due to fluctuating concentration profiles. We evaluate sampling of sewer overflows (0.3–14 h) with Chemcatcher and 1-week sampling in rivers. The uncertainty due to fluctuating concentrations is small, and other factors such as chemical analyses and sampler calibration have a similar or higher impact. The uncertainty due to fluctuations clearly increases with the sampling duration, particularly when exceeding the half-life of equilibrium. We conclude that passive sampling can also be used in wastewater systems with potentially high concentration variations.