AI-Driven Analysis of Meteorological and Emission Characteristics Influencing Urban Smog: A Foundational Insight into Air Quality
AIを活用した都市スモッグに影響する気象・排出特性の分析：大気質への基礎的洞察 (AI 翻訳)
Sadaf Zeeshan, Muhammad Ali Ijaz Malik
🤖 gxceed AI 要約
日本語
本研究は、ラホールのスモッグ原因を機械学習（ランダムフォレスト、XGBoost）を用いて分析し、気象・排出データからAQIを高精度に予測。運輸部門が排出の約90%を占めることを特定し、政策立案に資する知見を提供する。
English
This study uses machine learning (Random Forest, XGBoost) to analyze the causes of smog in Lahore, predicting AQI with high accuracy from meteorological and emission data. It identifies the transportation sector as responsible for about 90% of emissions, offering insights for policy interventions.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本でも都市部の大気汚染対策にAI活用の可能性を示すが、GX（グリーントランスフォーメーション）との直接的な関連性は低い。
In the global GX context
This paper provides a machine-learning approach for urban air quality prediction, relevant globally for cities facing smog issues, though not directly tied to climate change mitigation or disclosure frameworks.
👥 読者別の含意
🔬研究者:機械学習による大気質予測手法に関心のある研究者に有用。
🏢実務担当者:都市環境担当者は、排出源特定と予測モデルを参考にできる。
🏛政策担当者:大気質改善施策の優先順位付けに活用可能。
📄 Abstract（原文）
In South Asia, smog has become a critical environmental concern that endangers public health, ecosystems, and the regional climate. To determine the primary causes of smog formation in Lahore during peak polluted months (October and November), the current study develops a dual analytical framework that combines cutting-edge machine learning with sector- and pollutant-specific emission analysis. To assess their relationship with Air Quality Index (AQI) and create a high-accuracy predictive model, meteorological factors and emission data from key sectors are used to build Random Forest and extreme gradient boosting (XGBoost) models. The current study evaluates the joint effects of weather and emission loads on AQI variability by integrating atmospheric dynamics with comprehensive emission profiles. The XGBoost model forecasts important pollutants from the transportation, industrial, and agricultural sectors, including carbon dioxide (CO2), oxides of nitrogen (NOx), Volatile Organic Compounds (VOCs), and particulate matter, in the second analytical tier. Particulate matter (PM), NOx, and transport-related pollutants are consistently identified by the models as the primary predictors of AQI, with high prediction performance. Furthermore, a 3-fold split is used for cross-validation, making sure that each fold maintained the data’s chronological order to avoid leakage. The model has modest root mean square error (RMSE) levels (4.32 and 8.14) and high coefficient of determination (R2) values (0.93–0.99). Approximately 90% of Lahore’s annual emissions resulted from the transportation sector. These results offer aid to policymakers to anticipate air quality, identify important emission sources, and execute targeted initiatives to minimize smog and promote a healthier urban environment. The current study also helps in analyzing the causes of atmospheric and sectoral pollution. While the study captures smog dynamics during peak pollution months, its temporal scope is limited, and finer spatial measurements could further improve the generalizability of the results.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/gases6010010first seen 2026-05-05 22:35:15
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