Construction of Kedisan Pier to Increase Tourist Visits and Water Quality in Lake Batur, Bali

Kedisan Pier gives a new feel of traveling in Batur, with a concept like a pier in South Korea, but also provides views of Lake Batur and Mount Batur simultaneously. Tourists not only visit restaurants, hot springs, hikes, and stay overnight but can also visit the Kedisan Pier Area. The increase in tourist visits influences the condition of water quality in Lake Batur. This study aims to determine the influence of the construction of Kedisan Pier on tourist visits and the quality of Lake Batur water. This study used a purposive sampling method; sampling was carried out at three points representing settlements and agriculture, water bodies/middle of lakes, and dock. The samples were tested for pH and temperature in the field and COD parameters in the laboratory. Furthermore, these three parameters are compared with Class 1 lake water quality standards in Government Regulation 22 of 2021. Based on the results, it is known that the pH and COD in the three locations exceed the class 1 water quality standard, which is 10 mg / L. pH in the range of 9.2 – 9.5. Increased COD concentration compared to the quality standard at point 1 by 127%; Point 2 is 82%, and Point 3 is 144%. Domestic activities cause the high pH and COD values at these three points—the highest COD value in the Kedisan Pier area. The construction of Kedisan Pier impacts the increasing number of tourists but also causes a decrease in Lake Batur's water quality. Based on this, human awareness is needed to increase tourism while maintaining the quality of waters for the future benefit of humans, flora, and fauna. In addition, further research needs to be carried out using other microbiological and chemical parameters to see the quality of Lake Batur waters

An Insight into Application of Land Use Land Cover Analysis towards Sustainable Agriculture within Jhajjar District, Haryana

The increasing population, depletion of natural resources, semi-arid climatic and poor soil health conditions in Jhajjar district of Haryana have drawn major attention towards the changes in Land Use/Land Cover (LULC). The region's increasing population is mainly dependent upon the agrarian economy; thus, sustainable agricultural production is a major thrust area of research. The present study analyses the LULC changes in the area during two decades 2000 – 2020, using remote sensing and Geographic Information System (GIS). Landsat satellite images (Landsat-7 and Landsat-8 satellites) for 2000 and 2020 were analyzed for mixed classification based on unsupervised classification followed by supervised classification. The study area has experienced an increase in agricultural land, surface water bodies and built-up land by 16.89%, 79.73% and 56.41%, respectively. There is a decrease in barren land and fallow land by 48.53% and 36.97%, respectively, as per the five major LULC classes. The LULC analysis indicates an increase in built-up land, which is responsible for controlling agricultural productivity and unsustainable agricultural activities. The study provides a comprehensive understanding of the land use trajectory in a specific region in two decades and associated unsustainable changes in the agrarian economy through pressure on the increase in agricultural production and conversion of land mass into croplands. It also signifies climate-resilient agriculture and the management of sustainable agriculture

A global metagenomic map of urban microbiomes and antimicrobial resistance

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.Funding: the Tri-I Program in Computational Biology and Medicine (CBM) funded by NIH grant 1T32GM083937; GitHub; Philip Blood and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1548562 and NSF award number ACI-1445606; NASA (NNX14AH50G, NNX17AB26G), the NIH (R01AI151059, R25EB020393, R21AI129851, R35GM138152, U01DA053941); STARR Foundation (I13- 0052); LLS (MCL7001-18, LLS 9238-16, LLS-MCL7001-18); the NSF (1840275); the Bill and Melinda Gates Foundation (OPP1151054); the Alfred P. Sloan Foundation (G-2015-13964); Swiss National Science Foundation grant number 407540_167331; NIH award number UL1TR000457; the US Department of Energy Joint Genome Institute under contract number DE-AC02-05CH11231; the National Energy Research Scientific Computing Center, supported by the Office of Science of the US Department of Energy; Stockholm Health Authority grant SLL 20160933; the Institut Pasteur Korea; an NRF Korea grant (NRF-2014K1A4A7A01074645, 2017M3A9G6068246); the CONICYT Fondecyt Iniciación grants 11140666 and 11160905; Keio University Funds for Individual Research; funds from the Yamagata prefectural government and the city of Tsuruoka; JSPS KAKENHI grant number 20K10436; the bilateral AT-UA collaboration fund (WTZ:UA 02/2019; Ministry of Education and Science of Ukraine, UA:M/84-2019, M/126-2020); Kyiv Academic Univeristy; Ministry of Education and Science of Ukraine project numbers 0118U100290 and 0120U101734; Centro de Excelencia Severo Ochoa 2013–2017; the CERCA Programme / Generalitat de Catalunya; the CRG-Novartis-Africa mobility program 2016; research funds from National Cheng Kung University and the Ministry of Science and Technology; Taiwan (MOST grant number 106-2321-B-006-016); we thank all the volunteers who made sampling NYC possible, Minciencias (project no. 639677758300), CNPq (EDN - 309973/2015-5), the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, ECNU, the Research Grants Council of Hong Kong through project 11215017, National Key RD Project of China (2018YFE0201603), and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01) (L.S.