Improving Ammonia Emission Modeling and Inventories by Data Mining and Intelligent Interpretation of the National Air Emission Monitoring Study Database

摘要: Abstract: Ammonia emission is one of the greatest environmental concerns in sustainable agriculture development. Several limitations and fundamental problems associated with current agricultural ammonia modeling inventories have been identified. They were a significant disconnection between field monitoring data knowledge about data. Comprehensive measurement datasets not fully exploited for scientific research regulations. This situation can be considerably improved if currently available are better interpreted new applied to update techniques. The world’s largest air quality database more than 2.4 billion points has recently created by United States’ National Air Emission Monitoring Study. New approaches mining intelligent interpretation planned uncover answer series questions that raised. expected results this idea include enhanced understanding emissions from animal accuracy scope regional national inventories.