PubMed · 16522940
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Dale Preston. 2006-03-07. Techa River studies.. https://doi.org/10.1088/0952-4746%2F26%2F1%2Fe07
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Parkinson's disease (PD) is a multifactorial chronic progressive neurodegenerative disease influenced by age, and by genetic and environmental factors. The role of genetic predisposition in PD has been increasingly acknowledged and a number of relevant genes have been identified (e.g., genes encoding alpha-synuclein, parkin, and dardarin), while the search for environmental factors that influence the pathogenesis of PD has only recently begun to escalate. In recent years, the investigation on paraquat (PQ) toxicity has suggested that this herbicide might be an environmental factor contributing to this neurodegenerative disorder. Although the biochemical mechanism through which PQ causes neurodegeneration in PD is not yet fully understood, PQ-induced lipid peroxidation and consequent cell death of dopaminergic neurons can be responsible for the onset of the Parkinsonian syndrome, thus indicating that this herbicide may induce PD or influence its natural course. PQ has also been recently considered as an eligible candidate for inducing the Parkinsonian syndrome in laboratory animals, and can therefore constitute an alternative tool in suitable animal models for the study of PD. In the present review, the recent evidences linking PQ exposure with PD development are discussed, with the aim of encouraging new perspectives and further investigation on the involvement of environmental agents in PD.
The groundwater samples collected from the shallow and deep groundwater aquifers of an industrial area of the Kanpur city (Uttar Pradesh, India) were analyzed for the concentration levels and distribution pattern of nitrogenous species, such as nitrate-nitrogen (NO(3)-N), nitrite-nitrogen (NO(2)-N), ammonical-nitrogen (NH(4)-N), organic-nitrogen (Org-N) and total Kjeldahl-nitrogen (TKN) to identify the possible contamination source. Geo-statistical approach was adopted to determine the distribution and extent of the contaminant plume. In the groundwater aquifers NO(3)-N, NO(2)-N, NH(4)-N, TKN, Org-N and Total-N ranged from 0.10 to 64.10, BDL (below detection limit)-6.57, BDL-39.00, 7.84-202.16, 1.39-198.97 and 8.89-219.43 mg l(-1), respectively. About 42% and 26% of the groundwater samples of the shallow and deep groundwater aquifers, respectively, exceeded the BIS (Bureau of Indian Standards) guideline value of 10 mg l(-1) for NO(3)-N and may pose serious health hazards to the people of the area. The results of the study revealed that the groundwater aquifers of the study area are highly contaminated with the nitrate and indicates point source pollution of nitrate in the study area.
Reducing racial/ethnic and socioeconomic environmental health disparities requires a comprehensive multilevel conceptual and quantitative approach that recognizes the various levels through which environmental health disparities are produced and perpetuated. We propose a conceptual framework that incorporates the micro level, contained within the local level, which in turn is contained within the macro level. We discuss the utility of multilevel techniques to examine environmental level (both physical and social) and individual-level factors to appropriately quantify and improve our understanding of environmental health disparities. We discuss the reasoning and the methodological approach behind multilevel modeling, including differentiating between individual and contextual influences on individual outcomes. Next we address the questions and principles that guide the choice of levels or geographic units in multilevel studies. Finally, we address the ways in which different data sources can be combined to produce suitable data for multilevel analyses. We provide some examples of how such data sources can be linked to create multilevel data structures, and offer suggestions to facilitate the integration of multilevel techniques in environmental health disparities research and monitoring.