关于潮湿侵害
运输中每年都有成千上万的货物受到潮湿的侵害。运达地点时, 金属可能已生锈或氧化, 纸箱会变软, 发霉, 木箱上长一层黑色的霉层。集装箱中散装的咖啡、可可、豆类在运达目的地时, 有可能潮湿、发霉, 不能食用, 这类损失有时高达数百万美元。这主要是由货物汗和集装箱雨造成的。
粮食发霉
集装箱潮湿侵害四大重点区域:
1.集装箱顶部和内壁,是集装箱雨的密集地带;
2.货物或货物包装表面,是货物汗的密集地带;
3.集装箱地板,是造成托盘发霉的主要潮湿来源。如果是铁质地板,很容易返潮;如果是木质地板;地板本身就含有水份;两者均易导致托盘发霉。
4.集装箱门,因外界空气有可能通过门缝进入,造成相对湿度较高。
集 装 箱 雨
托 盘 发 霉
术语小贴士:
集装箱雨:当货物穿越不同经纬度地区,集装箱温湿度发生变化时,空气中的湿气和水分将会在集装箱的顶部和内壁凝成液态水珠,不断滴落如同下雨,称之为“集装箱雨”。
货物汗:当货物穿越不同经纬度地区,集装箱温湿度发生变化时,空气中的湿气和水分将会在货物表面或其包装上凝成液态水珠,如同人体出汗,称之为“货物汗”。
结露点:是指物体表面开始结露形成液态水的温度临界点。当物体表面的温度等于或低于露点时,就会产生结露。如果相对湿度下降了,结露点温度也会相应下降。
相对湿度:即空气中所包含的水汽重量和在该温度下空气中最大能包含的水汽重量的比值。
一定温度和相对湿度下每立方米空气中的水汽含量
RH T(℃) |
100% | 90% | 80% | 70% | 60% | 50% | 40% | 30% | 20% | 10% |
65 | 160.3 | 144.3 | 128.2 | 112.2 | 96.2 | 80.2 | 64.1 | 48.1 | 32.1 | 16.0 |
60 | 129.6 | 116.6 | 103.7 | 90.7 | 77.8 | 64.8 | 51.8 | 38.9 | 25.9 | 13.0 |
55 | 103.9 | 93.5 | 83.1 | 72.7 | 62.3 | 52.0 | 41.6 | 31.2 | 20.8 | 10.4 |
50 | 82.7 | 74.4 | 66.2 | 57.9 | 49.6 | 41.4 | 33.1 | 24.8 | 16.5 | 8.3 |
45 | 65.2 | 58.7 | 52.2 | 45.6 | 39.1 | 32.6 | 26.1 | 19.6 | 13.0 | 6.5 |
40 | 50.9 | 45.8 | 40.7 | 35.6 | 30.5 | 25.5 | 20.4 | 15.3 | 10.2 | 5.1 |
35 | 39.2 | 35.3 | 31.4 | 27.4 | 23.5 | 19.6 | 15.7 | 11.8 | 7.8 | 3.9 |
30 | 30.0 | 27.0 | 24.0 | 21.0 | 18.0 | 15.0 | 12.0 | 9.0 | 6.0 | 3.0 |
25 | 22.8 | 20.5 | 18.2 | 16.0 | 13.7 | 11.4 | 9.1 | 6.8 | 4.6 | 2.3 |
20 | 17.1 | 15.4 | 13.7 | 12.0 | 10.3 | 8.6 | 6.8 | 5.1 | 3.4 | 1.7 |
15 | 12.7 | 11.4 | 10.2 | 8.9 | 7.6 | 6.4 | 5.1 | 3.8 | 2.5 | 1.3 |
10 | 9.3 | 8.4 | 7.4 | 6.5 | 5.6 | 4.7 | 3.7 | 2.8 | 1.9 | 0.9 |
5 | 6.8 | 6.1 | 5.4 | 4.8 | 4.1 | 3.4 | 2.7 | 2.0 | 1.4 | 0.7 |
0 | 4.8 | 4.3 | 3.8 | 3.4 | 2.9 | 2.4 | 1.9 | 1.4 | 1.0 | 0.5 |
-5 | 3.2 | 2.9 | 2.6 | 2.2 | 1.9 | 1.6 | 1.3 | 1.0 | 0.6 | 0.3 |
-10 | 2.1 | 1.9 | 1.7 | 1.5 | 1.3 | 1.1 | 0.8 | 0.6 | 0.4 | 0.2 |
-15 | 1.4 | 1.3 | 1.1 | 1.0 | 0.8 | 0.7 | 0.6 | 0.4 | 0.3 | 0.1 |
-20 | 0.9 | 0.8 | 0.7 | 0.6 | 0.5 | 0.5 | 0.4 | 0.3 | 0.2 | 0.1 |
根据上表我们能计算出在海运前集装箱/纸箱里空气水分的含量 声明: |
不同地区温度、降雨量和降雨天数记录
中国北京 | 中国上海 | ||||||||
Temperature | Total | Days | Temperature | Total | Days | ||||
Month | Lowest | Highest | Rainfall | Rainfall | Month | Lowest | Highest | Rainfall | Raining |
/day | /day | mm | Days | /day | /day | mm | Days | ||
Jan | -9.4 | 1.6 | 3 | 2 | Jan | 0.5 | 7.7 | 39 | 9 |
Feb | -6.9 | 4.0 | 6 | 3 | Feb | 1.5 | 8.6 | 59 | 10 |
Mar | -0.6 | 11.3 | 9 | 4 | Mar | 5.1 | 12.7 | 81 | 13 |
Apr | 7.2 | 19.9 | 26 | 5 | Apr | 10.6 | 18.6 | 102 | 13 |
May | 13.2 | 26.4 | 29 | 6 | May | 15.7 | 23.5 | 115 | 13 |
Jun | 18.2 | 30.3 | 71 | 9 | Jun | 20.3 | 27.2 | 152 | 14 |
Jul | 21.6 | 30.8 | 176 | 14 | Jul | 24.8 | 31.6 | 128 | 12 |
Aug | 20.4 | 29.5 | 182 | 12 | Aug | 24.7 | 31.5 | 133 | 10 |
Sep | 14.2 | 25.8 | 49 | 7 | Sep | 20.5 | 27.2 | 156 | 12 |
Oct | 7.3 | 19 | 19 | 5 | Oct | 14.7 | 22.3 | 61 | 9 |
Nov | -0.4 | 10.1 | 6 | 3 | Nov | 8.6 | 16.7 | 51 | 8 |
Dec | -6.9 | 3.3 | 2 | 2 | Dec | 2.4 | 10.6 | 35 | 7 |
中国广州 | 中国厦门 | ||||||||
Temperature | Total | Days | Temperature | Total | Days | ||||
Month | Lowest | Highest | Rainfall | Lowest | Month | Lowest | Highest | Rainfall | Lowest |
/day | /day | mm | /day | /day | /day | mm | /day | ||
Jan | 9.8 | 18.3 | 43 | 8 | Jan | 9.7 | 16.8 | 37 | 8 |
Feb | 11.3 | 18.4 | 65 | 11 | Feb | 9.8 | 16.5 | 65 | 13 |
Mar | 14.9 | 21.6 | 85 | 15 | Mar | 11.9 | 18.8 | 99 | 17 |
Apr | 19.1 | 25.5 | 182 | 16 | Apr | 16.1 | 23 | 147 | 16 |
May | 22.7 | 29.4 | 284 | 18 | May | 20.3 | 26.7 | 152 | 16 |
Jun | 24.5 | 31.3 | 258 | 19 | Jun | 23.3 | 29.4 | 196 | 14 |
Jul | 25.3 | 32.7 | 228 | 16 | Jul | 25.3 | 32.4 | 140 | 10 |
Aug | 25.2 | 32.6 | 221 | 16 | Aug | 25.2 | 32.2 | 155 | 10 |
Sep | 23.8 | 31.4 | 172 | 13 | Sep | 23.8 | 30.7 | 117 | 11 |
Oct | 20.5 | 28.6 | 79 | 7 | Oct | 20.5 | 27.4 | 29 | 3 |
Nov | 15.7 | 24.4 | 42 | 6 | Nov | 16.4 | 23.4 | 37 | 5 |
Dec | 11.1 | 20.5 | 24 | 5 | Dec | 11.7 | 19.1 | 25 | 4 |