An IoT-based waste management system monitored by cloud

Clever solid waste bin is important to create a well-organized and dynamic waste management system. This paper presents the novel way of carrying out an integrated sensing system which automates the solid waste management process. The proposed smart waste bin is based on ultrasonic-level sensor and various gas sensors which automatically sense the hazardous gases and the maximum limit of waste. The approach is unique and uses cloud and mobile app-based monitoring. Two important features of work are it not only checks the maximum waste level of the bin but also checks various stinky gases. The other part of the work is conveying the information to the responsible authority. This unique approach takes the assistance of cloud sever because of its advantages in field of usability, accessibility and disaster recovery. The information can be linked with municipality web server for immediate action. The waste bins are tracked by a unique number which represents its location. The eccentric technique gives all information related to physical condition of a particular bin and can easily reach the corresponding authority. The whole information is interconnected with a cloud-based web-information system at the host server.     

Improving land use inference by factorizing mobile phone call activity matrix

Land use is shaped by human activities. Traditional methods of measuring land uses (e.g. surveys and remote sensing techniques) often have difficulties in capturing human activities. The near-ubiquitous coverage of mobile phones opens up a new way to investigate land use through human activities. We propose to analyze land use by characterizing human activity patterns based on the aggregated call volume, and apply non-negative matrix factorization to identify fundamental behavioral classes. Using tower-based call data from Dakar, Senegal, we discover two fundamental land use patterns: commercial/business/industrial (C/B/I) and residential. Then, the land use of the reception area of each cell tower can be inferred based on the weights obtained for each basis vector. To evaluate the proposed approach, the results are compared with two points-of-interest (POI) data sets obtained from OpenStreetMap and Facebook’s Graph API. We have found that a majority of POIs like embassies, offices, and hotels are located in the predicted C/B/I areas; specifically, there is a strong positive correlation between estimated land use weights and the number of related POIs. Furthermore, we have shown analyzing 24-h call pattern matrix can track daily land use changes.     

Urinary fluoride as a monitoring tool for assessing successful intervention in the provision of safe drinking water supply in five fluoride-affected villages in Dhar district, Madhya Pradesh, India

Endemic fluorosis was detected in 31 villages in the Dhar district of Madhya Pradesh, Central India. Out of the 109 drinking water sources that were analyzed, about 67 % were found to contain high concentration of fluoride above the permissible level of 1.0 mg/l. Dental fluorosis among the primary school children in the age between 8 and 15 served as primary indicator for fluoride intoxication among the children. Urinary fluoride levels among the adults were found to be correlated with drinking water fluoride in 10 villages affected by fluoride. Intervention in the form of alternate safe water supply in five villages showed significant reduction in the urinary fluoride concentration when compared to the control village. Urinary fluoride serves as an excellent marker for assessing the effectiveness of intervention program in the fluoride-affected villages.     

Multiple stressors,food system vulnerability and food insecurity in Humla,Nepal

Regional Environmental Change - As the impacts of climate change (CC) have become apparent, Nepal has recently implemented a number of programs emphasizing climate sensitive resource management and...     

Complete dechlorination of endosulfan and lindane using Mg0/Pd(+4) bimetallic system

A Mg0/Pd(+4) bimetallic system was evaluated to dechlorinate endosulfan and lindane in the aqueous phase. Studies were conducted with endosulfan and lindane separately, with or without acid in a 1:1 (v/v) water:acetone phase. In the absence of any acid, higher degradation of endosulfan and lindane was observed using Mg0/Pd(+4) doses of 10/0.5 and 4/0.1 mg/mL, respectively. Acetone plays an important role in facilitating the dechlorination reaction by increasing the solubilities of pesticides. Dechlorination kinetics for endosulfan and lindane (30 and 50 mg/L [30 and 50 ppm] concentration of each pesticide) were conducted with varying Mg0/Pd(+4) doses, and the time-course profiles were well-fitted into exponential curves. The optimum observed rate constants (k(obs)) for endosulfan and lindane were obtained with Mg0/Pd(+4) doses of 5/0.5 and 4/0.1 mg/mL, respectively. Gas chromatography-mass spectrometry analyses revealed that endosulfan and lindane were dechlorinated completely into their hydrocarbon skeletons-Bicyclo [2,2,1] hepta 2-5 diene and benzene, respectively.     

Assessment of groundwater quality: a fusion of geochemical and geophysical information via Bayesian neural networks

Deplorable quality of groundwater arising from saltwater intrusion, natural leaching and anthropogenic activities is one of the major concerns for the society. Assessment of groundwater quality is, therefore, a primary objective of scientific research. Here, we propose an artificial neural network-based method set in a Bayesian neural network (BNN) framework and employ it to assess groundwater quality. The approach is based on analyzing 36 water samples and inverting up to 85 Schlumberger vertical electrical sounding data. We constructed a priori model by suitably parameterizing geochemical and geophysical data collected from the western part of India. The posterior model (post-inversion) was estimated using the BNN learning procedure and global hybrid Monte Carlo/Markov Chain Monte Carlo optimization scheme. By suitable parameterization of geochemical and geophysical parameters, we simulated 1,500 training samples, out of which 50 % samples were used for training and remaining 50 % were used for validation and testing. We show that the trained model is able to classify validation and test samples with 85 % and 80 % accuracy respectively. Based on cross-correlation analysis and Gibb’s diagram of geochemical attributes, the groundwater qualities of the study area were classified into following three categories: “Very good”, “Good”, and “Unsuitable”. The BNN model-based results suggest that groundwater quality falls mostly in the range of “Good” to “Very good” except for some places near the Arabian Sea. The new modeling results powered by uncertainty and statistical analyses would provide useful constrain, which could be utilized in monitoring and assessment of the groundwater quality.     

On stochastic differential equations and equilibrium distribution: a conditional moment approach

In the recent past there have been several attempts to obtain the equilibrium distribution of multiple populations and their moments in the context of some biological or ecological processes (e.g., Matis and Kiffe in Biometrics 52:155, 1996; Matis and Kiffe in Environ Ecol Stat 9:237, 2002; Renshaw in J Math Appl Med Biol, 15:1, 1998). In particular, the method of cumulant truncation (Matis and Kiffe in Biometrics 52:155, 1996) is a pioneering work in this field. However it requires solving a large number of cumulant functions even in the case of two simultaneous differential equations. Besides the solutions are approximate and depend on the precision of the software. Renshaw (Math Biosci 168:57, 2000) provided a nice extension of the univariate truncated saddle point procedure to multivariate scenarios. But this approach involves a multivariate Newton-Raphson type iterative algorithm whose performance and convergence are critically dependent on the choice of the initial values. In the present paper we propose a new and simple approach to obtain the equilibrium distribution of populations and their conditional moments in a system of differential equations of any dimension. Our proposed method, which is a natural extension of the classical variational matrix approach, has several advantages which are discussed in detail in the paper; among other things it includes the derivation of additional conditions which can be interpreted as environmental surrogates.     

Evaporative Cooling of Water in a Small Vessel Under Varying Ambient Humidity

Evaporative cooling of water in a small porous clay vessel was studied under controlled humidity conditions. In steady-state experiments performed at an ambient temperature of 23 °C, the cooling effect increased from 4.7 to 8.3 °C as the ambient relative humidity decreased from 60 to 15%. External heat and mass transfer coefficients, estimated from the steady-state measurements, were used in mathematical models to predict the experimentally observed transient temperature variation of the water under ramp changes of the ambient relative humidity. With a prototypical cool chamber containing water tested in Kolkata, India under an ambient temperature of 34.5–35 °C, the cooling effect reached a maximum of 7 °C between 3 and 3:30 PM and then declined to 4.5 °C around 6 PM.