36 3 3 2013 3 Environmental Science & Technology 王攀, 任连海 *, 甘筱 100048 Vol. 36 No.3 May. Mar. 2013 4 : 4 4 265 163 579 355 t/d 4 73.67%~78.27% 0.93%~2.48% 9.04%~15.59% GDP > > GDP> 323 A doi 10.3969/j.issn.1003-6504.2013.03.038 1003-6504(2013)03-0181-05 Investigation and Output Factors Analysis of Restaurant Garbage for Cities in China WANG Pan, REN Lian-hai *, GAN iao School of Food and Chemical Engineering, Beijing Technology and Business University, Beijing 100048, China AbstractQingdao, Jiaxing, Guiyang and ining were chosen as the cities on behalf of different districts of China. The investigation of restaurant garbage output was carried out by the form of questionnaires and on-site tracking, and the components of restaurant garbage of four cities were analyzed. Investigation results show that restaurant garbage outputs of Qingdao, Jiaxing, Guiyang, and ining are estimated as 265, 163, 579 and 355 t/d. Water contents, combustibles contents and nutrient contents of restaurant garbage in the four cities are 73.67%~78.27%, 0.93%~2.48%, 9.04%~15.59% respectively. Oil contents of restaurant garbage in ining and Guiyang, and sundries contents of restaurant garbage in Qingdao and Jiaxing are relatively high. The gray relational analysis method was used to calculate the relational degree between the output and effect factors, such as population size, sex ratio, GDP per capita, per capita consumption of city people, per capita food consumption. The relational degree between effect factors and restaurant garbage output is in the order as population size> sex ratio> per capita food consumption > GDP per capita > per capita consumption of city people. Key words restaurant garbage; output survey; component analysis; gray relational analysis [4-5] [1] [2] 4 [3] 2012-09-20 2012-12-03 201109035 QNJJ2012-23 1983- wangpan024@163.com * renlianhai@yahoo.com.cn
36 2 2 4 2 500 g 4 GB/T 5009.3-2003 4 GB/T 14772-2008 4 4 4 1.3 灰色关联度计算方法 4 1 1.1 产生量调查方法 [6] 1.3.1 4 0 ={x 0 (k) k=1,2, n} i ={x i (k) k=1,2, n}i=1 2 m 4 ε i (k)= min min x 0 (k)-x I (k) +pmax max x 0 (k)-x I (k) x 0 (k)-x i (k) +pmax max x 0 (k)-x i (k) 1 1 ε i (k) 0 i k min min 30 max max p (0,1) 15 <30 <15 0.5 4 1.3.2 2012 6-7 100 120 24 24 48 24 r i = 1 N Σ ε i ( k ) 2 N k = 1 2 r i i 103 110 101 109 r i x i x 0 p=0.5 r i 0.5 i 0 0.6>r i >0.5 i 0 0.8>r i >0.6 i 0 r i >0.8 1.2 餐厨垃圾成分检测 2 4 2.1 各城市餐厨垃圾产量分析 4 1 1 10 5 165 4 070 1 518 7 430
3 3 4 2 73.67%~78.27% 4 0.93%~2.48% 265 t 355 t 163 t 579 t 2 1 4 Table 1 The restaurant waste source of four cities 232 56 75 164 1 111 202 258 617 / 3 307 3 472 940 5 692 515 340 245 957 2 4 9.04%~15.59% 5 165 4 070 1 518 7 430 3 4 Table 3 Restaurant waste components of four cities (%) 2 4 Table 2 Restaurant waste output of four cities 75.51 5.53 0.93 5.35 12.67 78.27 9.31 1.91 1.47 9.04 153 257 179 224 95 144 126 122 /kg d -1 29 80 75 65 54 100 191 101 /t d -1 265 355 163 579 / 208 120 120 304 /kg ( d) -1 0.13 0.30 0.14 0.19 73.67 3.37 1.40 5.96 15.59 75.47 8.78 2.48 1.00 12.27 2.3 餐厨垃圾产量与影响因素的关联度分析 2.3.1 3 1 2 2.2 各城市餐厨垃圾成分分析 3 [9] 2 1 2 [7] 3, [8] GDP
36 4 4 5 0 1 i=1,2,3,4,5 6 5 5 4 Table 5 The initial-value transformation of restaurant waste output and impact factors Y 0 1 1.340 0.615 2.185 Y 1 1 0.577 0.577 1.462 > > > GDP Y 2 1 0.438 0.784 0.399 > > > Y 3 1 1.066 1.000 1.102 Y 4 1 0.537 0.942 0.738 Y 5 1 0.596 0.861 0.756 6 Table 6 The correlation coefficient ε 1 1 0.539 0.959 0.552 ε 2 1 0.497 0.841 0.333 ε 3 1 0.766 0.699 0.452 ε 4 1 0.527 0.732 0.382 ε 5 1 0.545 0.784 0.385, 2 r 1 =0.763 r 2 =0.668 r 3 =0.729 r 4 = 0.660 r 5 =0.679 r 1 >r 3 >r 5 >r 2 >r 4 [10] 2.3.2 > > 4 > GDP> 0 1 2 GDP 3 4 5 4 4 4 Table 4 Restaurant waste outputs and values of impact factors * /t d -1 0 265 355 163 579 / 1 208 120 120 304 GDP/ a -1 2 65 016 28 446 51 003 25 941 3 100.55 107.22 100.58 110.84 / 4 17 531 9 420 16 522 12 939 / 5 6 486 3 863 5 582 4 905 3 * 2010 1 4 265 355 163 579 t / d 2 4 4 ε i
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