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Appraisal of Noise Level Dissemination Surrounding Mining and Industrial Areas of Keonjhar, Odisha: a Comprehensive Approach Using Noise Mapping

Lokhande S. K., Dhawale S. A., Pathak S. S., Gautam R., Jain M. C., Bodhe G. L.

Archives of Acoustics

2017

Vol. 42, No. 3

423--432

Noise mapping is a well-established practice among the European nations, and it has been follow for almost two decades. Recently, as per guidelines of the Directorate General of Mines Safety (DGMS), India, noise mapping has been made mandatory in the mining expanses. This study is an effort to map the noise levels in nearby areas of mines in the northern Keonjhar district. The motive of this study is to quantify the existing A-weighted time-average sound level (LAeq, T) in the study area to probe its effects on the human dwellings and noise sensitive areas with the probability of future development of the mines, roads, and industrial and commercial zone. The LAeq, T was measured at 39 identified locations, including industrial, commercial, residential, and sensitive zones, 15 open cast mines, 3 major highways, and 3 haulage roads. With the utilisation of Predictor LimA Software and other GIS tools, the worked out data is mapped and noise contours are developed for the visualisation and identification of the extent and distribution of sound levels across the study area. This investigation discloses that the present noise level at 60% of the locations in silence and residential zone exposed to significantly high noise levels surpasses the prescribed limit of Central Pollution Control Board (CPCB), India. The observed day and night time LAeq, T level of both zones ranged between 43.2–62.2 dB(A) and 30.5–53.4 dB(A), respectively, whereas, the average Ldn values vary between 32.7 and 51.2 dB(A). The extensive mobility of heavy vehicles adjoining the sensitive areas and a nearby plethora of open cast mines is the leading cause of exceeded noise levels. The study divulges that the delicate establishments like schools and hospitals are susceptible to high noise levels throughout the day and night. A correlation between observed and software predicted values gives R2 of 0.605 for Ld, 0.217 for Ln, and 0.524 for Ldn. Finally, the mitigation measure is proposed and demonstrated using a contour map showing a significant reduction in the noise levels by 0–5.3 dB(A).

Time series analysis of reference crop evapotranspiration for Bokaro District, Jharkhand, India

Gautam R., Sinha A. K.

Journal of Water and Land Development

2016

no. 30

51--56

Evapotranspiration is the one of the major role playing element in water cycle. More accurate measurement and forecasting of Evapotranspiration would enable more efficient water resources management. This study, is therefore, particularly focused on evapotranspiration modelling and forecasting, since forecasting would provide better information for optimal water resources management. There are numerous techniques of evapotranspiration forecasting that include autoregressive (AR) and moving average (MA), autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), Thomas Feiring, etc. Out of these models ARIMA model has been found to be more suitable for analysis and forecasting of hydrological events. Therefore, in this study ARIMA models have been used for forecasting of mean monthly reference crop evapotranspiration by stochastic analysis. The data series of 102 years i.e. 1224 months of Bokaro District were used for analysis and forecasting. Different order of ARIMA model was selected on the basis of autocorrelation function (ACF) and partial autocorrelation (PACF) of data series. Maximum likelihood method was used for determining the parameters of the models. To see the statistical parameter of model, best fitted model is ARIMA (0, 1, 4) (0, 1, 1)12.

Ewapotranspiracja jest jednym z głównych elementów obiegu wody. Dokładniejsze pomiary i możliwość prognozowania ewapotranspiracji mogłyby umożliwić wydajniejsze zarządzanie zasobami wodnymi. Dlatego prezentowane w niniejszej pracy badania skoncentrowane były na modelowaniu i prognozowaniu ewapotranspiracji, ponieważ prognozowanie zapewni więcej informacji do optymalnego zarządzania zasobami wodnymi. Istnieje wiele technik prognozowania ewapotranspiracji, takich jak autoregresja (AR), średnia ruchoma (MA), autoregresyjna średnia ruchoma (ARMA), autoregresyjna zintegrowana średnia ruchoma (ARIMA), metoda Thomasa– Feiringa i inne. Stwierdzono, że spośród nich ARIMA jest bardziej odpowiednia do analizy i prognozowania zdarzeń hydrologicznych. Z tego powodu wykorzystano model ARIMA do prognozowania miesięcznych średnich wartości ewapotranspiracji potencjalnej poprzez analizę stochastyczną. Do analiz i prognozowania użyto serii danych ze 102 lat (1224 miesiące) z dystryktu Bokaro. Na podstawie funkcji autokorelacji (ACF) i cząstkowych autokorelacji (PACF) serii danych wybrano różny porządek modelu ARIMA. Do wyznaczenia parametrów modelu wykorzystano metodę maksymalnego prawdopodobieństwa. Najlepiej dostosowanymi parametrami statystycznymi modelu okazały się ARIMA (0, 1, 4) (0, 1, 1)12.