R Hussein*

Syracuse, NY, USA

*Corresponding Author: R Hussein, Syracuse, NY, USA.

Received: August 19, 2022; Published: September 19, 2022

Abstract

This paper coins the concept of data-driven municipal solid waste management (DDMSWM). The waste sector aims to achieve goals such as reduction, reuse, recycling, recovery, and disposal of waste for the preservation of natural and built environments, including energy, climate, and water and soil resources. Our original concept distinguishes between DDMSWM and traditional sustainable materials management. The latter term refers to matters that are sent to the landfill or municipal combustors.

The municipal solid waste (MSW) industry (MSWI) collects massive amounts of heterogeneous and unpredictable waste-related data to analyze and, accordingly, make management and operational decisions. This paper debates some strategic issues of the MSWI's data-related practices, such as the following: 1. The modern world is rapidly changing, with an unpredictable future. Who could have imagined the recent international waste-related new policies, epidemics, conflicts, and the state of the economy, just to name a few examples? At the national [1], state [2], and county [3] levels, the US Centers for Disease Control and Prevention (CDC) collected the COVID-19 data and aggregated it on a weekly basis because of the unpredictability issue. The logical question is thus: can the massive amounts of seasonably collected MSW data from the modern world be relied upon for making long-term strategies? The approach of the MSWI to data exploration needs to be reconsidered starting from its foundation, i.e., data. 2. The previously mentioned collected MSW data and its quantitative judgments suggest the MSWI pays attention to the understanding of various facets of waste such as composition, characterization, rates, generation, and differences. On the one hand, the disseminated outcomes obtained from the MSW collected data [4,7-13] clearly show the complex and diverse inherited characteristics of the MSW data. On the other hand, the same literature suggests the use of simple conventional analytic tools to analyze that kind of data. The logical question is: by putting the two hands together, what is the significance of the outcomes disseminated for industrial MSW practices? In addition, in today's economically stressed MSWI [4] and a world that runs on live streaming technologies, can the MSWI afford to continue doing business-as-usual? 3. The same literature suggests the MSWI is paying attention to trends in waste-related goals, e.g., zero waste, efficient waste collection, waste reduction, recycling, and resource recovery. The logical question is: how can the goals of the US's Federal Resource Conservation Act (RCRA) [5] and New York State Municipal Solid Waste (MSW) [6-8] frameworks, plans, acts, and other goals be achieved using simple conventional numerical data manipulation?

There are other relevant questions than the above to ask and observations to note. However, Albert Einstein once said, "In the middle of difficulty lies an opportunity". Whether the collected data and generated outcomes indicate a favorable trend [7-12] or not, the MSWI must adapt its analytic tools to today's world and technologies rather than a world that no longer exists. Our vision ensures that viable decisions are made in response to the unprecedented challenges as they occur.

A key objective of this paper is to introduce a systematic review of the futuristic concept of data-analytics-driven robust DDMSWM to the MSWI, in general, to benefit from the corporations that have been on the data-driven technology path and to revamp its practices.

Keywords: Data Analytics; Data Science; Municipal Waste; Waste Management; Resource Recovery; Recycling; STEM