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COVID-19 is a highly infectious disease caused by SARS-CoV-2, first identified in China and spread globally, resulting into pandemic. Transmission of virus takes place either directly through close contact with infected individual (symptomatic/asymptomatic) or indirectly by touching contaminated surfaces. Virus survives on the surfaces from few hours to days. It enters the human body through nose, eyes or mouth. Other sources of contamination are faeces, blood, food, water, semen etc. Parameters such as temperature/relative humidity also play an important role in transmission. As the disease is evolving, so are the number of cases. Proper planning and restriction are helping in influencing the trajectory of the transmission. Various measures are undertaken to prevent infection such as maintaining hygiene, using facemasks, isolation/quarantine, social/physical distancing, in extreme cases lockdown (restricted movement except essential services) in hot spot areas or throughout the country. Countries that introduced various mitigation measures had experienced control in transmission of COVID-19. Python programming is conducted for change point analysis (CPA) using Bayesian probability approach for understanding the impact of restrictions and mitigation methods in terms of either increase or stagnation in number of COVID-19 cases for eight countries. From analysis it is concluded that countries which acted late in bringing in the social distancing measures are suffering in terms of high number of cases with USA, leading among eight countries analysed. The CPA week in comparison with date of lockdown and first reported case strongly correlates (Pearson’s r = − 0.86 to − 0.97) to cases, cases per unit area and cases per unit population, indicating earlier the mitigation strategy, lesser the number of cases. The overall paper will help the decision makers in understanding the possible steps for mitigation, more so in developing countries where the fight against COVID-19 seems to have just begun.

The coastal water quality of Mumbai is deteriorating due to various point and non-point wastewater sources. Hence, it is desirable to monitor coastal water quality for various water-related activities like bathing, contact water sports, recreation, and commercial fishing. The objective of this paper is to assess the seasonal water quality on the basis of seawater standards. Based on water-quality analysis of 17 seafronts and beaches, most of the parameters were exceeding the standards. The statistical cluster analysis was carried out for evaluating impact of wastewater and sewage discharges. The hierarchical cluster analysis resulted into three clustered groups, namely less polluted, moderately polluted, and highly polluted sites with similar characteristics of water quality. Mahim was found to be worst-affected beach due to incoming organic load from the Mithi river in comparison to other seafronts and beaches. Unaccounted sources of sewage and wastewater should be identified and rerouted through sewerage system by improving collection efficiency, treatment, and proper disposal for achieving designated receiving water quality standards.

Variability of groundwater quality parameters is linked to various processes such as weathering, organic matter degradation, aerobic respiration, iron reduction, mineral dissolution and precipitation, cation exchange and mixing of salt water with fresh water. Multivariate statistical analyses such as principal component analysis (PCA) and hierarchical cluster analysis (HCA) were applied to the standardized data set of eleven groundwater quality parameters (i.e. pH, Ca2+, Mg2+, Na+, K+, Fe3+, alkalinity, NO3-, Cl-, SO4(2-), TDS) collected during the post-monsoon and the summer seasons in order to elicit hydrologic and biogeochemical processes affecting water quality in the unconfined aquifer beneath Puri city in eastern India. The application of PCA resulted in four factors explaining 73% variance in post-monsoon and 81% variance in summer. The HCA using Ward's method and squared Euclidean distance measure classified the parameters into four clusters based on their similarities. PCA and HCA allowed interpretation of processes. During both post-monsoon and summer seasons, anthropogenic pollution and organic matter degradation/Fe(III) reduction were found dominant due to contribution from on-site sanitation in septic tanks and soak pits in the city. Cation exchange and mineral precipitation were possible causes for increase in Na+ and decrease in Ca2+ concentration in summer. Fresh water recharge during monsoon and Sea water intrusion in summer are attributed as significant hydrologic processes to variations of the groundwater quality at the study site.

Puri City is situated on the east coast of India and receives water supply only from the groundwater sources demarcated as water fields. The objective of this paper is to assess and evaluate the groundwater quality due to impact of anthropogenic activities in the city. Groundwater samples were collected from the water fields, hand pumps, open wells, and open water bodies during post-monsoon 2006 and summer 2007. Groundwater quality was evaluated with drinking water standards as prescribed by Bureau of Indian Standards and Environmental Protection Agency to assess the suitability. The study indicated seasonal variation of water-quality parameters within the water fields and city area. Groundwater in the water fields was found to be suitable for drinking after disinfection. While in city area, groundwater quality was impacted by onsite sanitary conditions. The study revealed that groundwater quality was deteriorated due to the discharge of effluent from septic tanks, soak pits, pit latrines, discharges of domestic wastewater in leaky drains, and leachate from solid waste dumpsite. Based on observed groundwater quality, various mitigation measures were suggested to protect the water fields and further groundwater contamination in the city.

BackgroundIn context of increasing traffic noise in urban India, the objective of the research study is to assess noise due to heterogeneous traffic conditions and the impact of honking on it.MethodTraffic volume, noise levels, honking, road geometry and vehicular speed were measured on national highway, major and minor roads in Nagpur, India.ResultsInitial study showed lack of correlation between traffic volume and equivalent noise due to some factors, later identified as honking, road geometry and vehicular speed. Further, frequency analysis of traffic noise showed that honking contributed an additional 2 to 5 dB (A) noise, which is quite significant. Vehicular speed was also found to increase traffic noise. Statistical method of analysis of variance (ANOVA) confirms that frequent honking (p < 0.01) and vehicular speed (p < 0.05) have substantial impact on traffic noise apart from traffic volume and type of road.ConclusionsThe study suggests that honking must also be a component in traffic noise assessment and to identify and monitor “No Honking” zones in urban agglomerations.

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Solid waste management systems currently receive wide attention, from both economic and environmental planners, because of their complexity during coordination of various management strategies. The efficiency and cost effectiveness of route optimization and disposal site selection depend largely upon the appropriate placement of storage bins and their corresponding command area for waste contribution. The present paper illustrates a geographic information system (GIS) based algorithm for optimal location and number of storage bins, considering p-median constrained model, based on Indian guidelines for Municipal Solid Waste Rules. The algorithm also computes the contributing command area of solid waste to a particular bin, based on the shortest distance, with descending slope for ease in solid waste collection.Résumé : Les systèmes de gestion des déchets solides attirent présentement beaucoup l'attention des planificateurs économiques et environnementaux en raison de leur complexité durant la coordination de diverses stratégies de gestion. L'efficacité et la rentabilité de l'optimization de route et la sélection du site d'enfouissement dépendent en grande partie de l'emplacement approprié des bacs de stockage et leur zone desservie correspondante pour la collecte de déchets. Le présent article présente un algorithme basé sur un système d'information géographique (SIG) utilisé pour déterminer l'emplacement et le nombre optimaux de bacs de stockage, compte tenu d'un modèle p-médian contraint, basé sur les directives concernant la réglementation des déchets solides municipaux en Inde. L'algorithme calcule également la zone de collecte de déchets solides desservie par un bac de stockage particulier en se basant sur la plus courte distance, avec une pente descendante pour faciliter la collecte des déchets solides.Mots-clés : déchets municipaux solides, emplacement des bacs, zone desservie, système d'information géographique.[Traduit par la Rédaction]