A study of contaminated land in São Paulo city, Brazil and mainly adopted remediation process face a deficient database

Lino, Juliana dos Santos; Aquino, Antônio Eduardo de

doi:10.11159/ijepr.2019.003

Cited by 1 publication

(1 citation statement)

References 14 publications

(11 reference statements)

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“…In addition to the low concentration of UGSs, these regions had a higher concentration of contaminated areas, mainly due to the presence of service stations and industries. Groundwater was the factor most affected by contaminated soils, which can negatively affect the public water supply in the city [38]. São Paulo is the fourth most-polluted city in Brazil.…”

Section: Supervised Classificationmentioning

confidence: 99%

Using a Novel Green Index to Support Ecosystem Services in a Megacity

Sant’Anna Neto,

Lana,

Lucas

et al. 2023

Forests

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We present a novel and efficient approach that enables the evaluation of environmental quality in cities worldwide using high-resolution satellite imagery, based on a new green index (GI) through multivariate analysis, to compare the proportion of urban green spaces (UGSs) with built and impervious surfaces. High-resolution images were used to perform a supervised classification of 25 districts in the city of São Paulo, Brazil. Only 11 districts showed higher urban forests, green spaces, green index, and green vs. built values, and impervious surface proportions with lower impervious and built spaces. On the other hand, the remaining districts had higher population densities and unfavorable conditions for urban ecosystem development. In some cases, urban green spaces were three-times smaller than the built and impervious surfaces, and none of the districts attained a high green quality index (0.75 to 1). Artificial intelligence techniques improved the precise identification of land cover, particularly vegetation, such as trees, shrubs, and grasses. The development of a novel green index, using multivariate statistical analysis, enhanced positive interactions among soil cover classes, emphasizing priority areas for enhancing environmental quality. Most of them should be prioritized by decision makers due to the low environmental quality, as identified by the low green index and worse ecosystem services, well-being, and health outcomes. The method can be employed in many other cities to enhance urban ecosystem quality, well-being, and health. The green index and supervised classification can characterize pastures, degraded forest fragments, and guide forest restoration techniques in diverse landscapes.

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Section: Supervised Classificationmentioning

confidence: 99%