GIS based interpolation method to urinary metal concentrations in Malaysia

Hybrid electrocoagulation reactor for dairy wastewater treatment and methodology for sludge reusability for the development of vermicompost

The dairy industry is a high-water-consuming sector, making water conservation crucial, especially in countries like India that are top milk producers and have large populations. This research evaluates the performance and effluent characterization of dairy effluent treatment systems in Kerala using modern, cost-effective technology to address this issue. A hybrid electrocoagulation reactor (HECR) is designed to operate simultaneously in coagulation, and the electrocoagulation process is proposed in this study. The wastewater treatment parameters of the HECR with those of six existing dairy effluent treatment plants using geological mapping and geographic information system (GIS) were studied. According to inverse distance weighted (IDW) interpolation findings, HECR's effluent treatment profile is comparable to existing technologies, with a minimum required retention time of 9.58 h. The results show the HECR treatment efficiency, removing up to 75.15% of turbidity and 94.5% of COD. Moreover, the sludge generated from HECR is transformed into nutrient-rich vermicompost by adding suitable fillers in the ratio of 5:1:3:1 (industrial dairy sludge/HECR sludge: cow dung, dry leaf, and seed vermicompost). The optimal conversion times for vermicompost developed from dairy industrial sludge are 40 days, with nitrogen, phosphorus, and potassium (NPK) values per gram vermicompost of 8.86%, 3.18%, and 3.6%, respectively. For HECR sludge, on the 60th day, the observed NPK value for the developed vermicompost is at 4.76%, 1.46%, and 5.1%. Overall, this research offers a promising solution to treat dairy wastewater and transform generated waste sludge into a valuable resource, highlighting the importance of technology adaptation in the dairy industry.

Multiscale Geographically Weighted Regression in the Investigation of Local COVID-19 Anomalies Based on Population Age Structure in Poland

A growing number of various studies focusing on different aspects of the COVID-19 pandemic are emerging as the pandemic continues. Three variables that are most commonly used to describe the course of the COVID-19 pandemic worldwide are the number of confirmed SARS-CoV-2 cases, the number of confirmed COVID-19 deaths, and the number of COVID-19 vaccine doses administered. In this paper, using the multiscale geographically weighted regression, an analysis of the interrelationships between the number of confirmed SARS-CoV-2 cases, the number of confirmed COVID-19 deaths, and the number of COVID-19 vaccine doses administered were conducted. Furthermore, using maps of the local R² estimates, it was possible to visualize how the relations between the explanatory variables and the dependent variables vary across the study area. Thus, analysis of the influence of demographic factors described by the age structure and gender breakdown of the population over the course of the COVID-19 pandemic was performed. This allowed the identification of local anomalies in the course of the COVID-19 pandemic. Analyses were carried out for the area of Poland. The results obtained may be useful for local authorities in developing strategies to further counter the pandemic.

Influence of the Demographic, Social, and Environmental Factors on the COVID-19 Pandemic-Analysis of the Local Variations Using Geographically Weighted Regression

As the COVID-19 pandemic continues, an increasing number of different research studies focusing on various aspects of the pandemic are emerging. Most of the studies focus on the medical aspects of the pandemic, as well as on the impact of COVID-19 on various areas of life; less emphasis is put on analyzing the influence of socio-environmental factors on the spread of the pandemic. In this paper, using the geographically weighted regression method, the extent to which demographic, social, and environmental factors explain the number of cases of SARS-CoV-2 is explored. The research was performed for the case-study area of Poland, considering the administrative division of the country into counties. The results showed that the demographic factors best explained the number of cases of SARS-CoV-2; the social factors explained it to a medium degree; and the environmental factors explained it to the lowest degree. Urban population and the associated higher amount and intensity of human contact are the most influential factors in the development of the COVID-19 pandemic. The analysis of the factors related to the areas burdened by social problems resulting primarily from the economic exclusion revealed that poverty-burdened areas are highly vulnerable to the development of the COVID-19 pandemic. Using maps of the local R2 it was possible to visualize how the relationships between the explanatory variables (for this research-demographic, social, and environmental factors) and the dependent variable (number of cases of SARS-CoV-2) vary across the study area. Through the GWR method, counties were identified as particularly vulnerable to the pandemic because of the problem of economic exclusion. Considering that the COVID-19 pandemic is still ongoing, the results obtained may be useful for local authorities in developing strategies to counter the pandemic.