scholarly journals Perubahan Kualitas Air Sungai Dan Waterborne Diseases Di Kabupaten Boyolali (Studi Air Sungai Gandul, Sungai Cemoro, Dan Sungai Pepe)

2021 ◽  
Vol 20 (2) ◽  
Author(s):  
Yusuf Afif
Keyword(s):  
Time Series ◽  
Fecal Coliform ◽  
Waterborne Diseases

Pencemaran lingkungan dapat terjadi akibat dari kegiatan industri, peternakan, pertanian dan aktivitas domestik. Terdapat beberapa parameter yang mengidentifikasikan pencemaran dan merugikan masyarakat serta ekosistem perairan antara lain BOD, COD, DO, Deterjen dan Fecal Colifrom. Pencemaran lingkungan pada air dapat menimbulkan berbagai macam penyakit di masyarakat yang tinggal sekitarnya. Penelitian data sekunder ini menganalisis parameter kualitas air sungai (BOD, COD, DO, Deterjen dan Fecal Coliform) dengan kejadian Waterborne diseases di Kabupaten Boyolali tahun 2017 – 2019. Penelitian ini merupakan penelitian jenis time series dengan rancangan penelitian data sekunder. Jenis data yang digunakan adalah data sekunder kualitas air sungai (Sungai Gandul di daerah Cepogo, Sungai Pepe di daerah Boyolali, dan Sungai Cemoro di daerah Simo) dan kejadian waterborne diseases di Kabupaten Boyolali. Data yang digunakan merupakan data tahunan selama 3 tahun yaitu dari 2017 hingga 2019. Data kualitas diolah dengan metode indeks pencemaran kemudian di analisis uji beda berpasangan wilcoxon serta pemetaan cluster – outlier Moran’s. Hasil perhitungan indeks pencemaran yang diperoleh kondisi dalam status tercemar ringan namun beberapa titik sungai mengalami kondisi cemar sedang.  Terdapat perbedaan yang signifikan antara kualitas air sungai saat musim hujan dengan musim kemarau. Risiko penyebaran waterborne diseases  tertinggi di  Kecamatan Wonosegoro dan Kecamatan Klego.

scholarly journals Escherichia coli (E. coli) as an Indicator of Fecal Contamination in Water: A Review

2019 ◽  
Author(s):  
Farhan Mohammad Khan ◽  
Rajiv Gupta
Keyword(s):  
Escherichia Coli ◽  
Water Quality ◽  
Drinking Water ◽  
Fecal Coliform ◽  
Fecal Contamination ◽  
Waterborne Diseases ◽  
Fecal Matter ◽  
E Coli ◽  
Detection Techniques ◽  
Pollution Indicator

Escherichia coli or E. coli is a member of the fecal coliform group and is a more specific indicator of fecal contamination than other fecal coliform species, its presence indicate possibly presence of harmful bacteria which will cause diseases and it also suggests the extent as well as the nature of the contaminants. E. coli bacteria able to survive in water for 4 – 12 weeks and at present, it appears as an indicator to provide the accurate bacterial contamination of fecal matter in drinking water, because of the availability of simple, affordable, fast, sensitive and exact detection techniques. According to the laboratory experiment based techniques, 24 - 48 hours are required for the bacterial concentration to be reported. So, there is a necessity for continuous monitoring. Techniques for detection of many pathogenic bacterial strains are not yet available, sometimes days to weeks are required to get the results. To overcome the difficulties, expensive and time-consuming techniques are required to detect, count and identify the presence of specific bacterial strain. Public health relies on online monitoring of water quality that depends majorly on examination of fecal indicator bacteria, thus protection of health requires fecal pollution indicator so that it is not required to analyze drinking water to overcome the problems associated with waterborne diseases. This paper will brief the classification, sources, survival of E. coli bacteria and its correlation with basic water quality parameters in water sources.```

scholarly journals Bacteriological and Physicochemical Quality of Drinking Water in Wegeda Town, Northwest Ethiopia

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Baye Sitotaw ◽  
Eshetie Melkie ◽  
Denekew Temesgen
Keyword(s):  
Drinking Water ◽  
Low Income ◽  
Water Samples ◽  
Physicochemical Parameters ◽  
Fecal Coliform ◽  
Pathogenic Bacteria ◽  
Scientific Data ◽  
Waterborne Diseases ◽  
Limit Values ◽  
Dug Well

Waterborne diseases continue to challenge communities in low-income countries like Ethiopia. Clinical information in Wegeda town showed that the prevalence of waterborne diseases was 58%. This study aimed to evaluate bacteriological and physicochemical drinking water quality in Wegeda town. This study will add valuable scientific data for future intervention. Water samples from protected and unprotected springs, hand-dug well, taps, and households’ containers were collected from November 2018 to June 2019 for bacteriological and physicochemical analyses. Besides, information about the potential risk factors was collected using a structured questionnaire. A total of 120 water samples were collected and analyzed for total and fecal coliform counts using the multiple tube fermentation method (MPN). The presence of Escherichia coli was also checked from fecal coliform positive samples collected from households’ containers. Selected physicochemical parameters were also determined using the standard methods. In all cases, the median values of total and fecal coliform counts ranged from 5 to 27 and 2 to 13 MPN/100 ml, respectively. Accordingly, all of the drinking water samples did not comply with the standards. Coliforms were significantly higher in the households’ containers than in the sources ( p < 0.05 ) and also significantly varied by water sources. The highest and lowest coliform counts were recorded in unprotected spring and taps, respectively. Besides, 18.33% of water samples collected from households’ containers were tested positive for E. coli. Regarding physicochemical parameters, most values were within the acceptable limit values recommended by the WHO. However, water samples from unprotected spring and hand-dug well did not satisfy the turbidity limit value set by the WHO. Drinking water systems in Wegeda town were likely contaminated with pathogenic bacteria likely due to poor protection and sanitation practices. Providing the community with potable water, toilets, domestic and animal waste disposal systems, and intensive health education and sanitation practices for the community are highly recommended.

The LDE-Type Flare Occurrence (1969-1993)

1994 ◽  
Vol 144 ◽  
pp. 279-282
Author(s):  
A. Antalová
Keyword(s):  
Time Series ◽  
Fourier Analysis ◽  
Sunspot Cycle ◽  
List Type ◽  
Type Number ◽  
Solar Cycles ◽  
Type Iv ◽  
Long Duration ◽  
Cycle Maximum ◽  
Flare Index

AbstractThe occurrence of LDE-type flares in the last three cycles has been investigated. The Fourier analysis spectrum was calculated for the time series of the LDE-type flare occurrence during the 20-th, the 21-st and the rising part of the 22-nd cycle. LDE-type flares (Long Duration Events in SXR) are associated with the interplanetary protons (SEP and STIP as well), energized coronal archs and radio type IV emission. Generally, in all the cycles considered, LDE-type flares mainly originated during a 6-year interval of the respective cycle (2 years before and 4 years after the sunspot cycle maximum). The following significant periodicities were found:• in the 20-th cycle: 1.4, 2.1, 2.9, 4.0, 10.7 and 54.2 of month,• in the 21-st cycle: 1.2, 1.6, 2.8, 4.9, 7.8 and 44.5 of month,• in the 22-nd cycle, till March 1992: 1.4, 1.8, 2.4, 7.2, 8.7, 11.8 and 29.1 of month,• in all interval (1969-1992):a)the longer periodicities: 232.1, 121.1 (the dominant at 10.1 of year), 80.7, 61.9 and 25.6 of month,b)the shorter periodicities: 4.7, 5.0, 6.8, 7.9, 9.1, 15.8 and 20.4 of month.Fourier analysis of the LDE-type flare index (FI) yields significant peaks at 2.3 - 2.9 months and 4.2 - 4.9 months. These short periodicities correspond remarkably in the all three last solar cycles. The larger periodicities are different in respective cycles.

Automatic Identification of Autoregressive Integrated Moving Average Time Series

1982 ◽  
Vol 14 (3) ◽  
pp. 156-166 ◽  
Author(s):  
Chin-Sheng Alan Kang ◽  
David D. Bedworth ◽  
Dwayne A. Rollier
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Automatic Identification ◽  
Autoregressive Integrated Moving Average
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