Title

Municipal Solid Waste Characterizations And Management Strategies For The Lower Rio Grande Valley, Texas

Abstract

The Lower Rio Grande Valley (LRGV or Valley) in Texas, facing the big waste management challenge along the US-Mexico border today, is at the crossroads as a result of the rapid population growth, the scarcity of landfill space, the bi-nation's trade impacts, and the illusive goal of environmental sustainability. This paper offers a unique municipal solid waste investigation with regard to both physical and chemical characteristics leading to illuminate the necessary management policies with greater regional relevancy. With multiple sampling campaigns conducted during the spring of 2005, this study holistically summarizes the composition of solid waste, the statistical distribution patterns of key recyclable items, and the heating value in an uncertain environment. Research findings indicate that high fractions of plastics and paper in the waste stream imply a strong potential for energy recovery. Incineration options are thus bolstered by mildly high heating values across 10 cities in this region, which may lead to save land resources required for final disposal and increase electricity generation in the long run. Additional regression analyses further identify the correlation between recyclable items and heating value, which show that current recycling programs permit no obvious negative impacts on the incineration option. Final statistical hypothesis tests for both the Brownsville-Harlingen-San Benito and the McAllen-Edinburg-Mission metropolitan regions help foster consistent management strategies across the Valley regardless of the trivial differences of waste characteristics in between. © 2007 Elsevier Ltd. All rights reserved.

Publication Date

2-29-2008

Publication Title

Waste Management

Volume

28

Issue

5

Number of Pages

776-794

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.wasman.2007.04.002

Socpus ID

39749196823 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/39749196823

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