No abstract

No abstract

Asia’s coastlines are choking in waste. The region is now home to many of the world’s most polluted beaches. The populous Indian Cities are growing economically but in an unsustainable manner. With Mumbai counted among topmost polluted beaches in the world, it is the need of the hour to take necessary steps for effective waste management by systematic data analysis for deriving useful information from waste generation patterns. The major objective of the study is pattern recognition and beach waste quantum prediction based on 5 years data, with a frequency of daily waste collection. The size of the training data set is 1,661 days and the validation data set is 335 days. The influence of population trend, waste generation during festivals, special days, weekends, and seasonal variations form the basis for the analysis. Using machine learning algorithms, the study identifies and investigates data patterns for the case study of Dadar-Mahim beach. Data frequency and weights are correlated with occurrence of events, festivals, weekends, and seasons. Exploratory Data Analysis (EDA) is employed for data preprocessing and wrangling, followed by a Random Forest algorithm-based model for the prediction of waste generated at Dadar-Mahim beach. The major challenges in data prediction are limited data availability and variation in the dates of festivals and holidays as well as lack of waste segregation information. Despite the above-mentioned challenges, the observations indicate the model’s average accuracy for making predictions of around 60%. The Graphic User Interface (GUI) developed based on the model provides a user-friendly application for predicting the total daily generation of beach waste with reasonable precision. On the basis of the model’s outcome and applicability, a schematic approach for efficient beach waste management is proposed. The recommendations would serve as guidelines for Urban Local Bodies (ULBs) to automate the collection, transport, and disposal of beach waste.

No abstract

The Sustainable Development Goals (SDG) 2015, which are defined to achieve improved and more sustainable future, promotes safe and affordable drinking water facilities for all, till 2030. The rural and remotely placed population worldwide face the problem of shortage of pathogen-free drinking water due to the huge capital and maintenance costs involved in water purification. The current chemical disinfection treatment which is widely used for water disinfection has several disadvantages including the formation of Disinfection By-Products (DBPs). Electro-chlorination is one of the best alternatives as per the literature that can be installed as a decentralized system in a remote location and can overcome most of the issues related to chemical disinfection. The present paper provides an overview of the innovations in the area of electro-chlorination as a disinfection technique through a detailed patent analysis. The patenting activity and publications are considered as an indicator of research and innovation in the field. The patent analysis is also supported by literature analysis for understanding the research trends and the extent of research in the area. The patent data from the year 2000 to 2021 is analyzed country-wise and year-wise. The paper also discusses the IPC, CPC codes, assignees, investors, US class codes, patents types, and citations analysis for the patents in the field of electro-chlorination and DBPs. The keywords used for patent analysis are ‘Electro- chlorination’ and ‘Water’ and ‘Disinfection’ and ‘Disinfection by Products’.