相关实验视频

Updated: Jun 6, 2025

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Published on: December 23, 2022

在城市范围内评估PM_2.5空气污染的驱动因素,使用可解释的机器学习

Yali Hou¹, Qunwei Wang², Tao Tan³

¹College of Information Engineering, Nanjing Xiaozhuang University, Nanjing 211171, China.

Waste management (New York, N.Y.)

|December 2, 2024

概括

中国中国中国中国.

科学领域:

环境科学环境科学
数据科学数据科学数据科学
城市规划城市规划

背景情况:

城市细颗粒物 (PM2.5) 污染阻碍了中国的可持续发展目标.
识别PM2.5驱动因素对于有效的减少污染战略至关重要.

研究的目的:

开发和验证一个机器学习模型来分析城市PM2.5度.
确定PM2.5污染的主要社会经济和工业驱动因素.
建议城市具体的PM2.5减少策略.

主要方法:

使用了 CatBoost 和树结构的 Parzen 估计器 (TPE) 机器学习模型.
采用了夏普利添加式解释 (SHAP) 来确定因素的重要性.
分析了2000年至2021年间来自中国297个城市的PM2.5数据.

主要成果:

该模型在预测城市PM2.5.5时获得了高准确度 (R2=96.44%).
社会经济因素和工业活动被确定为主要驱动因素.
氧化排放,技术投资,人口密度和能源消耗是2000年的重要因素.

结论:

为未来的污染控制,建议采取有针对性的战略,重点关注人口密度和采矿开发.
该框架为评估污染因素和定制减少策略提供了一个强大的工具.
由于减少了氧化排放,观察到空气质量的显著改善.

关键词:

空气污染大气污染机器学习机器学习在PM{2.5) 中,莎普利的添加式解释树结构的帕森估计器

更多相关视频

Assessing the Particulate Matter Removal Abilities of Tree Leaves

Assessing the Particulate Matter Removal Abilities of Tree Leaves

Published on: October 7, 2018

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions

Published on: January 7, 2019

相关概念视频

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...

Sampling Plans

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...