India has rich mineral resources, and some of them occupy an important positionin the world market. The productions of chromite, coal, and barite rank third in the world, while the production of iron oreranks fourth. And bauxite and manganese ore productions rank sixth in the world. In order to process these mineral resources into small pieces, we need to use a variety of professional ore mining equipment. SBM is a professional stone crusher manufacturer, and it has more than 20 years experience. Because of perfect performance, our products efficient stone crushers are widely used in the ore crushing project in India. Here we will introduce a limestone crushing project and a pebble crushing project in India for you.
In 2012, a customer from India contacted with our customer service staff online. Through communication we learned that he wanted to buy a complete crushing and screening line for processing limestone. The size of the raw material is about 200 mm. And he required that the final particle size of 30 mm,and the production capacity can reach 250 tons / hour. This Indian customer also reflected that the mining site is relatively poor, so he had to find another venue to set up production line.
After a detailed understanding, our experienced engineers designed a complete limestone crushing line according to the customers specific situation and requirement of capacity and final grain size. Because the size of the raw material is about 200 mm, so there is no need for primary crushing. We can send to raw limestone to impact crusher directly for fine crushing. In this way, it can save investment of the jaw crusher. Due to the hardness of the limestone is not high, so we recommend customer to use our PFW1315 impact crusher.
Of course, we need add a vibrating feeder to feed limestone continuously and evenly. And we also need a vibrating screen to screen the final products into different sizes. At the same time, belt conveyor is used to connect the machines and improve the production lines automation degree. For the environment of mining site is poor and cant build production line, we recommend customer to adopt our mobile impact crusher. It including all of the above equipment, and it can directly reach the scene to conduct mining operations. In this way, it can reduce the amount of infrastructure and transportation costs. This Indian user was satisfied with our plan and bought our mobile impact crusher. He thought that our mobile crusher is withreasonable price, as well as perfect performance.
India has coal resources, the total coal reserves of about 240 billion tons. Because of rich coal resource, Indian coal industry has developed rapidly in recent year. India has become the third-largest coal producer in the world.
Recently, an Indian customer contacted with us. He already has a coal crushing line. Customers have reported that the production costs of this coal crushing line are very high, because the raw coal caused serious damage to the jaw plate, hammer, impact plate and other wear parts. All of these wear parts need to be replaced frequently, which greatly increasing the cost of production. So he wanted to build a new coal crushing line instead. For this Indian customer's requirements, we recommend him to choose jaw crusher and cone crusher. First of all, send the raw coal to jaw crusher by vibrating feeder evenly for primary crushing. After screening, the crushed materials which meet the required size will be transported to cone crusher. Eventually, the Indian customertook our suggestion, and bought a jaw crusher and a cone crusher from our company.
Particulate matter concentration by three to four times amid lockdown.Surface temperature is reduced by 35C.Amid lockdown noise level is reduced from 85dBA to <65dBA.Total dissolve solid concentration in river water is reduced by two times.
Stone quarrying and crushing spits huge stone dust to the environment and causes threats to ecosystem components as well as human health. Imposing emergency lockdown to stop infection of COVID 19 virus on 24.03.2020 in India has created economic crisis but it has facilitated environment to restore its quality. Global scale study has already proved the qualitative improvement of air quality but its possible impact at regional level is not investigated yet. Middle catchment of Dwarka river basin of Eastern India is well known for stone quarrying and crushing and therefore the region is highly polluted. The present study has attempted to explore the impact of forced lockdown on environmental components like Particulate matter (PM) 10, Land surface temperature (LST), river water quality, noise using image and field derived data in pre and during lockdown periods. Result clearly exhibits that Maximum PM10 concentration was 189 to 278 g/m3 in pre lockdown period and it now ranges from 50 to 60g/m3 after 18days of the commencement of lockdown in selected four stone crushing clusters. LST is reduced by 35 C, noise level is dropped to <65dBA which was above 85dBA in stone crusher dominated areas in pre lockdown period. Adjacent river water is qualitatively improved due to stoppage of dust release to the river. For instance, total dissolve solid (TDS) level in river water adjacent to crushing unit is attenuated by almost two times. When entire world is worried about the appropriate policies for abating environmental pollution, this emergency lockdown shows an absolute way i.e. pollution source management may restore environment and ecosystem with very rapid rate.
The present paper has intended to explore the noise level and vulnerability to noise produced in the stone mining and crushing area and the surroundings in the heavily stressed stone mining and crushing area of Middle catchment of Dwarka river basin of Eastern India. Field-based noise recording has been done at different times in every recorded days. Fuzzy logic-based weighting and integration of eight parameters have been done in four selected cluster for noise susceptibility mapping. For exploring noise annoyance odd and risk ratio have been computed for different communities. From the recorded noise level in stone crushing clusters, it is found that the noise level is>85dBA from 6a.m. to 4p.m. when the stone crushers are running on and it is above the ambient noise threshold defined by Central Pollution Control Board in 2000. Maximum noise level is recorded as 112.41dBA which may cause deafness. Noise intensity gradually decreases from crushing centre towards outside and it prevails up to 500650m away from the crushing unit. Noise vulnerable areas constructed based on eight noise level addressing and noise effect indicating parameters using fuzzy logic revealed that about 10.4627.98% areas fall under very high to high noise vulnerable zones. Therefore, the labourers and the peoples who are living at close proximity of crusher units are highly prone to noise pollution along with stone dust pollution. The effect of noise is highly age and sex sensitive due to their differences in physical strength. These findings could effectively be used for saving the exposed communities from noise vulnerability.
Afeni, T. B., & Osasan, S. K. (2009). Assessment of noise and ground vibration induced during blasting operations in an open pit mineA case study on Ewekoro limestone quarry, Nigeria. Mining Science and Technology (China), 19(4), 420424.
Armaghani, D. J., Hajihassani, M., Monjezi, M., Mohamad, E. T., Marto, A., & Moghaddam, M. R. (2015). Application of two intelligent systems in predicting environmental impacts of quarry blasting. Arabian Journal of Geosciences, 8(11), 96479665. https://doi.org/10.1007/s12517-015-1908-2.
Balashanmugam, P., Nehrukumar, V., Balasubramaniyan, K., & Balasubramanian, G. (2015). Effect of road traffic noise pollution in Cuddalore town: A case study.International Journal of Innovative Research in Science, Engineering and Technology. Research and Reviews,21.
Basu, T., & Pal, S. (2018). Identification of landslide susceptibility zones in Gish River basin, West Bengal, India. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 12(1), 1428.
Beutel, M. E., Jnger, C., Klein, E. M., Wild, P., Lackner, K., Blettner, M., & Mnzel, T. (2016). Noise annoyance is associated with depression and anxiety in the general populationThe contribution of aircraft noise. PLoS ONE, 11(5), e0155357. https://doi.org/10.1371/journal.pone.0155357.
Caniani, D., Lioi, D. S., Mancini, I. M., & Masi, S. (2011). Application of fuzzy logic and sensitivity analysis for soil contamination hazard classification. Waste Management, 31(3), 583594. https://doi.org/10.1016/j.wasman.2010.09.012.
Chawla, A., Chawla, S., Pasupuleti, S., Rao, A. C. S., Sarkar, K., & Dwivedi, R. (2018). Landslide susceptibility mapping in Darjeeling Himalayas, India. Advances in Civil Engineering. https://doi.org/10.1155/2018/6416492.
Chen, W., Xie, X., Peng, J., Wang, J., Duan, Z., & Hong, H. (2017). GIS- based landslide susceptibility modeling assessment of kernel logistic regression, Nave-Bayes tree, and alternating decision tree models. Geomatics, Natural Hazards and Risk, 8(2), 950973. https://doi.org/10.1080/19475705.2017.1289250.
CPCB. (2000). The Noise pollution (Regulation and Control) Rules, 2000. Noise standards, Central Pollution Control Board,Ministry of Environment and Forest. Government of India. Retrieved October 15, 2013, from http://www.cpcb.nic.in/divisionsofheadoffice/pci2/noise_rules_2000.pdf.
Das, P., Talukdar, S., Ziaul, S., Das, S., & Pal, S. (2019). Noise mapping and assessing vulnerability in meso level urban environment of Eastern India. Sustainable cities and societies. https://doi.org/10.1016/j.scs.2019.01.001.
de Kluizenaar, Y., Janssen, S. A., Vos, H., Salomons, E. M., Zhou, H., & van den Berg, F. (2013). Road traffic noise and annoyance: A quantification of the effect of quiet side exposure at dwellings. International Journal of Environmental Research and Public Health, 10(6), 22582270. https://doi.org/10.3390/ijerph10062258.
Di Lorenzo, L., Coco, V., Forte, F., Trinchese, G. F., Forte, A. M., & Pappagallo, M. (2014). The use of odds ratio in the large population-based studies: Warning to readers. Muscles, Ligaments and Tendons Journal, 4(1), 90.
Ercanoglu, M., & Gokceoglu, C. (2002). Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environmental Geology, 41(6), 720730. https://doi.org/10.1007/s00254-001-0454-2.
Farzana, S. Z., Nury, A. H., Biswas, B., & Das, A. (2014). A study on noise pollution of stone crusher machine at Jaflong, Sylhet. InProceedings of the 5th international conference on environmental aspects of Bangladesh(pp. 3032).
Hajihassani, M., Armaghani, D. J., Sohaei, H., Mohamad, E. T., & Marto, A. (2014). Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Applied Acoustics, 80, 5767. https://doi.org/10.1016/j.apacoust.2014.01.005.
Ismail, A. F., Daud, A., Ismail, Z., & Abdullah, B. (2013). Noise-induced hearing loss among quarry workers in a north-eastern state of Malaysia: A study on knowledge, attitude and practice. Oman Medical Journal, 28(5), 331. https://doi.org/10.5001/omj.2013.96.
Jakovljevic, B., Paunovic, K., & Belojevic, G. (2009). Road-traffic noise and factors influencing noise annoyance in an urban population. Environment International., 35, 552556. https://doi.org/10.1016/j.envint.2008.10.001.
Jiang, W., Lv, J., Wang, C., Chen, Z., & Liu, Y. (2017). Marsh wetland degradation risk assessment and change analysis: A case study in the Zoige Plateau, China. Ecological Indicators, 82, 316326. https://doi.org/10.1016/j.ecolind.2017.06.059.
Jindal, H., Saxena, S., & Kasana, S. S. (2018). A sustainable multi-parametric sensors network topology for river water quality monitoring. Wireless Networks, 24(8), 32413265. https://doi.org/10.1007/s11276-017-1532-z.
Lautenschlager, K., Hwang, C., Liu, W. T., Boon, N., Kster, O., Vrouwenvelder, H., & Hammes, F. (2013). A microbiology-based multi-parametric approach towards assessing biological stability in drinking water distribution networks. Water Research, 47(9), 30153025. https://doi.org/10.1016/j.watres.2013.03.002.
Mandal, I., & Pal, S. (2020b). Modelling human health vulnerability using different machine learning algorithms in stone quarrying and crushing areas of Dwarka river Basin Eastern India. Advances in Space Research, 66(6), 13511371.
Mehdi, M. R., Kim, M., Seong, J. C., & Arsalan, M. H. (2011). Spatio-temporal patterns of road traffic noise pollution in Karachi, Pakistan. Environment International, 37, 97104. https://doi.org/10.1016/j.envint.2010.08.003.
Murphy, E., King, E. A., & Rice, H. J. (2009). Estimating human exposure to transport noise in central Dublin, Ireland. Environment International, 35(2), 298302. https://doi.org/10.1016/j.envint.2008.07.026.
Niemann, H., & Maschke, C. (2004). WHO LARES Final report Noise effects and morbidity.Berlin: World Health Organisation, t1 omlsningerogudfordringer. http://www.gate21.dk/wpcontent/uploads/2016/05/Hvidbog_samlet_web.pdf.
Nikolaou, A. D., Meric, S., Lekkas, D. F., Naddeo, V., Belgiorno, V., Groudev, S., & Tanik, A. (2008). Multi-parametric water quality monitoring approach according to the WFD application in Evros trans-boundary river basin: Priority pollutants. Desalination, 226(13), 306320. https://doi.org/10.1016/j.desal.2007.02.113.
Nirmalya, M., & Gandhari, B. (2011). Occupational deafness of workers in a heavy engineering industry of West Bengal, India: An in-depth cross-sectional study. Sudanese Journal of Public Health, 6(3), 9197.
Okokon, E. O., Turunen, A. W., Ung-Lanki, S., Vartiainen, A. K., Tiittanen, P., & Lanki, T. (2015). Road-traffic noise: Annoyance, risk perception, and noise sensitivity in the Finnish adult population. International Journal of Environmental Research and Public Health, 12(6), 57125734. https://doi.org/10.3390/ijerph120605712.
Omubo-Pepple, V. B., & Tamunobereton-ari, I. (2011). Noise induced hearing loss within the Rivers State University of Science and Technology, Port Harcourt, Nigeria. Journal of Basic and Applied Scientific Research, 1(8), 868874.
Onder, M., Onder, S., & Mutlu, A. (2012). Determination of noise induced hearing loss in mining: An application of hierarchical loglinear modelling. Environmental Monitoring and Assessment, 184(4), 24432451.
Pal, S. (2016). Identification of soil erosion vulnerable areas in Chandrabhaga river basin: A multi-criteria decision approach. Modeling Earth Systems and Environment, 2(1), 5. https://doi.org/10.1007/s40808-015-0052-z.
Pal, S., & Debanshi, S. (2018). Influences of soil erosion susceptibility toward overloading vulnerability of the gully head bundhs in Mayurakshi River basin of eastern Chottanagpur Plateau. Environment, Development and Sustainability, 20(4), 17391775.
Pal, S., & Mandal, I. (2017). Impacts of stone mining and crushing on stream characters and vegetation health of Dwarka River Basin of Jharkhand and West Bengal, Eastern India. Journal of Environmental Geography, 10(12), 1121. https://doi.org/10.1515/jengeo-2017-0002.
Pal, S., & Mandal, I. (2019a). Impact of aggregate quarrying and crushing on socio-ecological components of Chottanagpur plateau fringe area of India. Environmental Earth Sciences, 78(23), 661. https://doi.org/10.1007/s12665-019-8678-1
Pandey, V. K., Pourghasemi, H. R., & Sharma, M. C. (2018). Landslide susceptibility mapping using maximum entropy and support vector machine models along the Highway Corridor, Garhwal Himalaya. Geocarto International. https://doi.org/10.1080/10106049.2018.1510038.
Paneto, G. G., de Alvarez, C. E., & Zannin, P. H. T. (2017). Relationship between urban noise and the health of users of public spacesA case study in Vitoria, ES, Brazil. Journal of Building Construction and Planning Research, 5(02), 45.
Rasyid, A. R., Bhandary, N. P., & Yatabe, R. (2016). Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia. Geoenvironmental Disasters, 3(1), 19.
Saha, T. K., & Pal, S. (2019). Exploring physical wetland vulnerability of Atreyee river basin in India and Bangladesh using logistic regression and fuzzy logic approaches. Ecological Indicators, 98, 251265.
Sami, M., Shiekhdavoodi, M. J., Pazhohanniya, M., & Pazhohanniya, F. (2014). Environmental comprehensive assessment of agricultural systems at the farm level using fuzzy logic: A case study in cane farms in Iran. Environmental Modelling & Software, 58, 95108.
Sayara, T. (2016). Environmental impact assessment of quarries and stone cutting industries in Palestine: Case study of Jammain. Journal of Environment Protection and Sustainable Development, 2(4), 3238.
Son, J. Y., Lee, J. T., Kim, H., Yi, O., & Bell, M. L. (2012). Susceptibility to air pollution effects on mortality in Seoul, Korea: A case-crossover analysis of individual-level effect modifiers. Journal of Exposure Science and Environmental Epidemiology, 22(3), 227.
Srensen, H. T., Horvath-Puho, E., Lash, T. L., Christiansen, C. F., Pesavento, R., Pedersen, L., et al. (2011). Heart disease may be a risk factor for pulmonary embolism without peripheral deep venous thrombosis. Circulation, 124(13), 14351441.
Srensen, M. (2016). Trafikstjens sundhedseffekter. Trafikstj et overset samfundsproblem. En hvidbog om lsninger og udfordringer. Available here: http://www.gate21.dk/wpcontent/uploads/2016/05/Hvidbog_samlet_web.pdf.