關于潮濕侵害
運輸中每年都有成千上萬的貨物受到潮濕的侵害。運達地點時, 金屬可能已生銹或氧化, 紙箱會變軟, 發霉, 木箱上長一層黑色的霉層。集裝箱中散裝的咖啡、可可、豆類在運達目的地時, 有可能潮濕、發霉, 不能食用, 這類損失有時高達數百萬美元。這主要是由貨物汗和集裝箱雨造成的。
糧食發霉
集裝箱潮濕侵害四大重點區域:
1.集裝箱頂部和內壁,是集裝箱雨的密集地帶;
2.貨物或貨物包裝表面,是貨物汗的密集地帶;
3.集裝箱地板,是造成托盤發霉的主要潮濕來源。如果是鐵質地板,很容易返潮;如果是木質地板;地板本身就含有水份;兩者均易導致托盤發霉。
4.集裝箱門,因外界空氣有可能通過門縫進入,造成相對濕度較高。
集 裝 箱 雨
托 盤 發 霉
術語小貼士:
集裝箱雨:當貨物穿越不同經緯度地區,集裝箱溫濕度發生變化時,空氣中的濕氣和水分將會在集裝箱的頂部和內壁凝成液態水珠,不斷滴落如同下雨,稱之為“集裝箱雨”。
貨物汗:當貨物穿越不同經緯度地區,集裝箱溫濕度發生變化時,空氣中的濕氣和水分將會在貨物表面或其包裝上凝成液態水珠,如同人體出汗,稱之為“貨物汗”。
結露點:是指物體表面開始結露形成液態水的溫度臨界點。當物體表面的溫度等于或低于露點時,就會產生結露。如果相對濕度下降了,結露點溫度也會相應下降。
相對濕度:即空氣中所包含的水汽重量和在該溫度下空氣中最大能包含的水汽重量的比值。
一定溫度和相對濕度下每立方米空氣中的水汽含量
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 |
根據上表我們能計算出在海運前集裝箱/紙箱里空氣水分的含量 聲明: |
不同地區溫度、降雨量和降雨天數記錄
中國北京 | 中國上海 | ||||||||
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 |
中國廣州 | 中國廈門 | ||||||||
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 |