Testing and training data sets for: A novel representation of time-resolved particle emissions from pyrolyzing wood

Biomass burning is responsible for emitting 90% of total primary organic aerosols into the atmosphere. Products from pyrolysis are released to the atmosphere when gas-phase reactions are unable to oxidize them, and even narrow windows of release can produce a large fraction of the overall particle emissions. This work introduces an approach to predict particle emission during high-emitting periods of biomass burning. We trained a regression-based machine learning model to predict particle emission using data from pyrolysis experiments covering seven wood types under controlled conditions. The model considered experimental mass-loss rate (MLR) of the wood, wood density, and heating conditions as features for prediction of measured particle-phase real-time emissions. After training, the experimental mass-loss rate was replaced with modeled MLR from a two-dimensional finite volume model of pyrolysis to predict particle emission rate using only wood properties and the boundary conditions of..., Data collection is described in the associated article and in a related article: M. Fawaz, A. Avery, T.B. Onasch, L.R. Williams, T.C. Bond, Technical note: Pyrolysis principles explain time-resolved organic aerosol release from biomass burning, Atmospheric Chemistry and Physics. 21 (2021) 15605–15618., , # Testing and training data sets for \"A novel representation of time-resolved particle emissions from pyrolyzing wood\" [https://doi.org/10.5061/dryad.rbnzs7hk6](https://doi.org/10.5061/dryad.rbnzs7hk6) These files contain measured data from experiments in which solid wood biomass samples, considered thermally thick, were pyrolyzed at different temperatures under nitrogen gas. Mass loss from the wood samples was measured at one-second intervals along with emission rates of pyrolysis products. Further details, especially of the particulate measurement, are contained in the referenced papers. Testing and training data sets were separated using a k-fold split. ## Description of the data and file structure Two files are given PyroPredict_training_set.csv: Data set used to train the gradient boosting regressor machine learning algorithm PyroPredict_testing_set.csv: Data set used to test the algorithm's output Each file contains the following columns: MLR: Mass loss rate (g/s) CO: Ca...生物质燃烧向大气排放的一次有机气溶胶占总排放量的90%。当气相反应无法将热解产物完全氧化时，这些产物会释放进入大气；即便释放窗口极为狭窄，也可能贡献总颗粒物排放的可观份额。本研究提出了一种预测生物质燃烧高排放时段颗粒物排放的方法。我们基于受控条件下覆盖7种木材的热解实验数据，训练了一款基于回归的机器学习模型以预测颗粒物排放。该模型以木材实验测得的质量损失率（mass loss rate, MLR）、木材密度以及加热条件作为特征，来预测实测的颗粒物相实时排放通量。训练完成后，我们将实验测得的质量损失率替换为二维有限体积热解模型模拟得到的MLR，仅通过木材属性与边界条件即可预测颗粒物排放速率，原文此处未完整给出边界条件相关内容。数据集的采集细节详见关联论文与以下参考文献：M. Fawaz、A. Avery、T.B. Onasch、L.R. Williams、T.C. Bond，技术简报：热解原理可解释生物质燃烧过程中时间分辨的有机气溶胶释放，《Atmospheric Chemistry and Physics》，21卷（2021年），15605–15618。# 用于《热解木材的时间分辨颗粒物排放新表征》一文的训练与测试数据集 [https://doi.org/10.5061/dryad.rbnzs7hk6] 本数据集包含的实验数据来自：在氮气氛围下、不同温度条件中对热厚固体木质生物质样品进行热解的实验。实验以1秒为间隔测量木材样品的质量损失，同时同步记录热解产物的排放速率。颗粒物测量的更多细节，详见参考文献。训练集与测试集通过k折划分法进行拆分。 ## 数据与文件结构说明 本次提供两个数据文件： PyroPredict_training_set.csv：用于训练梯度提升回归器机器学习算法的训练数据集 PyroPredict_testing_set.csv：用于测试该算法输出结果的测试数据集 每个文件均包含以下列字段： MLR：质量损失率（单位：g/s） CO：Ca...