Air Quality in Peace & War - Parma vs Kyiv using MATLAB and Raspberry Pi
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评估
- 难度
- 4/5
- 预计耗时
- 3-5 天
- 新手友好度
- 20/100
- Issue 类型
- 文档
- 描述清晰度
- 需要澄清
- 活跃度
- 冷清
- 技术栈
- matlab, raspberry-pi
- 领域
- data, machine-learning
调研方向
The issue describes a completed air quality project and links to its GitHub repository, but it does not request a specific change to this repository. Read the linked project and the repository’s contribution guidance to determine whether the authors intended a contribution or a project showcase; the issue provides no acceptance criteria for what would count as done.
由索引模型根据 Issue 内容生成。
描述
Esteem Professors,
We would like to share our project developed using MATLAB, Raspberry Pi, and an SDS011 sensor as part of our learning in statistical modelling, IoT, and real-world data analysis.
GitHub Repository: (https://github.com/Simran-2400/air-quality-peace-war.git)
Project Overview
The objective was to compare air quality between a peaceful city (Parma, Italy) and a city under active war (Kyiv, Ukraine) during the October 2023 missile attack. We collected 3,600 PM2.5/PM10 readings using a Raspberry Pi + SDS011 sensor in Parma, and used open-access SaveEcoBot ground sensor data for Kyiv. Statistical analysis was performed entirely in MATLAB.
Implemented Features
Time series visualisation of 3,600 readings per city
K-Means clustering (K=3) to classify Clean Air, Moderate, and Extreme pollution events
Elbow Method for optimal K selection
Linear regression PM10 ~ PM2.5 with 95% confidence band
Centroid verification using grpstats() and assert()
Technologies Used
MATLAB (Statistics & Machine Learning Toolbox)
Raspberry Pi + SDS011 sensor
SaveEcoBot open sensor network
GitHub for version control
Key Result
K-Means clustering isolated the October 27th missile attack as an independent extreme cluster — with no labels, no dates, no context provided. PM2.5 peak: 818 µg/m³ — 54× the WHO daily limit.
Teammates:
@AbeerAbuNemer · @ahmedelmenyawe96-ui · @emelian-chkaira
We sincerely thank @MarcoRianiUNIPR , @Asadunipr , and @AldoCorbelliniUNIPR for providing us the Raspberry Pi, their guidance, and for encouraging us to apply our learning to a real-world problem.
Thank you.
- 主要语言
- MATLAB
- 星标
- 30
- 派生
- 1
- PR 合并指标
- 30 天内没有已合并 PR
环境准备
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难度 1/5 1 小时以内 新手友好度 87/100
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难度 1/5 1 小时以内 新手友好度 77/100
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