ArticleEnvironmental monitoring and assessment2022
Analysis of indicators of surface water pollution in Atlantic Forest preservation areas.
Article in Environmental monitoring and assessment, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Managing water resources in regions with scarce data, like most developing countries, is still one of the major challenges around the world. Analysis of water quality parameters can provide important information for understanding the current status of water resources and their surroundings, including the changes that have occurred over time. This study aims to evaluate the influence of preservation areas on surface water quality in the Atlantic Forest biome. For this purpose, water quality monitoring sites with a greater number of parameters and longer monitoring time, located in six basins in the Atlantic Forest region of Brazil near preservation areas, were selected. This study employs seven statistical methods, such as cluster and principal component analysis (PCA), and promotes a robust analysis of the pollution of water resources in the Atlantic Forest. The most preserved basins, with more than 87% preservation area, have lower levels of pollution. The second most degraded basin, with 56% preservation area, presents intermediate pollution levels. The most degraded basin has the highest level of pollution. The basin with the lowest area of native vegetation is considered a degraded basin. Finally, non-point sources of pollution from agricultural activities were identified as the main sources of pollution in the region. The cophenetic correlation of 0.97 indicates a good performance of the cluster analysis. In addition, the pre-tests of PCA showed the suitability of the data for performing the test (Bartlett test, < 2.2e
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