نوع مقاله : مقاله کامل علمی پژوهشی
نویسندگان
1 دانشجوی دکتری هواشناسی کشاورزی، گروه مهندسی آبیاری و آبادانی، دانشکده مهندسی و فناوری کشاورزی، دانشگاه تهران،کرج، ایران
2 عضو هیئت علمی گروه تکنولوژی و مهندسی چوب، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، ایران.
3 عضو هیئت علمی گروه تکنولوژی و مهندسی چوب، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، ایران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Abstract
Equilibrium moisture content (EMC) of wood is one of the major indicators in wood industries. Determine of EMC in different locations of services, can prevent from wood waste due to meteorological parameters. This study was performed to estimate the relationship between EMC and the geo-statistical parameters (latitude, longitude and altitude). The results showed that the coastal and cool climate and locations have EMC higher than dry and central locations of Iran. The winter months, the early months of spring and fall months leading up to winter season have more EMC. The analysis showed that the spatial variations of the two variables latitude - height equation provide better results than the uni-variable equations. So, multivariable equations showed better results in compare with uni and two variable equations. The good results obtained in the multivariable equations in the warmer months. The best way to assess the EMC by spatial variables is regional scale study of EMC, inclusion of climatic conditions, remoteness and proximity to water sources and finally specific climate modeling for any region.
Abstract
Equilibrium moisture content (EMC) of wood is one of the major indicators in wood industries. Determine of EMC in different locations of services, can prevent from wood waste due to meteorological parameters. This study was performed to estimate the relationship between EMC and the geo-statistical parameters (latitude, longitude and altitude). The results showed that the coastal and cool climate and locations have EMC higher than dry and central locations of Iran. The winter months, the early months of spring and fall months leading up to winter season have more EMC. The analysis showed that the spatial variations of the two variables latitude - height equation provide better results than the uni-variable equations. So, multivariable equations showed better results in compare with uni and two variable equations. The good results obtained in the multivariable equations in the warmer months. The best way to assess the EMC by spatial variables is regional scale study of EMC, inclusion of climatic conditions, remoteness and proximity to water sources and finally specific climate modeling for any region.
The analysis showed that the spatial variations of the two variables latitude - height equation provide better results than the uni-variable equations. So, multivariable equations showed better results in compare with uni and two variable equations. The good results obtained in the multivariable equations in the warmer months. The best way to assess the EMC by spatial variables is regional scale study of EMC, inclusion of climatic conditions, remoteness and proximity to water sources and finally specific climate modeling for any region.
کلیدواژه‌ها [English]
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