RESUMO
This paper introduces an integrated workflow that effectively evaluates environmental and health risks of dioxin-like Persistent Organic Pollutants (dl-POPs) at industrial hotspot regions. The developments of validated, cost effective and user-friendly analytical strategies which can be field deployable are quintessential for routine monitoring of dl-POPs, particularly in developing countries. This study addresses the lacunae by enabling an exclusive gas chromatography triple quadrupole mass spectrometer based analytical workflow substituting conventional magnetic sector high resolution mass spectrometer technique and validated the methodology as per the European Union regulation 644/2017. The viable monitoring utility of the methodology for predicting enviro-food-health nexus was field-tested by analyzing fish and sediment samples from the Eloor-Edayar industrial belt, a solitary POPs hotspot in India. The profiles of congeners indicate that dl-POPs were formed through precursor pathways, suggesting the potential release of chlorinated precursor species from surrounding industrial area as the root cause. Fish samples from hotspots were observed to have 8 times higher levels of polychlorinated dibenzo-p-dioxin/furans (PCDD/Fs) and 30 times higher levels of polychlorinated biphenyls (PCBs) than the control sites. A strong statistically significant (p < 0.05) positive correlation was observed between dl-POPs levels in fish and sediment samples at the study site and the Biota sediment accumulation factors for PCDD/Fs and dl-PCBs ranged from 0.019 to 0.092 and 0.004 to 0.671 respectively. The estimated weekly intake from fish consumption in the study region was observed to be 3 to 24 times higher than the maximum levels set by the European food safety authority (2 pgTEQ kg-1bwweek-1). Hence, the periodic surveillance of dl-POPs employing user friendly/validated confirmatory tools stands highly imperative to safeguard human health and environment. Keywords: Dioxin and PCBs, GC-MS/MS, POPs Hotspot, Biota-sediment accumulation factor, Correlation analysis, Health risk assessment.
