Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability
Ozone pollution is profoundly modulated by meteorological features such as temperature, air pressure, wind, and humidity. While many studies have developed empirical models to elucidate the effects of meteorology on ozone variability, they predominantly focus on local weather conditions, overlooking the influences from high-altitude and broader regional meteorological patterns. Here, we employ convolutional neural networks (CNNs), a technique typically applied to image recognition, to investigate the influence of three-dimensional spatial variations in meteorological fields on the daily, seasonal, and interannual dynamics of ozone in Shenzhen, a major coastal urban center in China. Our optimized CNNs model, covering a 13° × 13° spatial domain, effectively explains over 70% of daily ozone variability, outperforming alternative empirical approaches by 7 to 62%. Model interpretations reveal the crucial roles of 2-m temperature and humidity as primary drivers, contributing 16% and 15% to daily ozone fluctuations, respectively. Regional wind fields account for up to 40% of ozone changes during the episodes. CNNs successfully replicate observed ozone temporal patterns, attributing -5-6 μg·m-3 of interannual ozone variability to weather anomalies. Our interpretable CNNs framework enables quantitative attribution of historical ozone fluctuations to nonlinear meteorological effects across spatiotemporal scales, offering vital process-based insights for managing megacity air quality amidst changing climate regimes.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
|---|---|
| Enthalten in: |
Environmental science & technology - (2024) vom: 14. März |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Mai, Zelin [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Convolutional neural networks |
|---|
| Anmerkungen: |
Date Revised 15.03.2024 published: Print-Electronic Citation Status Publisher |
|---|
| doi: |
10.1021/acs.est.3c07907 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM369756517 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM369756517 | ||
| 003 | DE-627 | ||
| 005 | 20240315234555.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240315s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1021/acs.est.3c07907 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1330.xml |
| 035 | |a (DE-627)NLM369756517 | ||
| 035 | |a (NLM)38485962 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Mai, Zelin |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 15.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status Publisher | ||
| 520 | |a Ozone pollution is profoundly modulated by meteorological features such as temperature, air pressure, wind, and humidity. While many studies have developed empirical models to elucidate the effects of meteorology on ozone variability, they predominantly focus on local weather conditions, overlooking the influences from high-altitude and broader regional meteorological patterns. Here, we employ convolutional neural networks (CNNs), a technique typically applied to image recognition, to investigate the influence of three-dimensional spatial variations in meteorological fields on the daily, seasonal, and interannual dynamics of ozone in Shenzhen, a major coastal urban center in China. Our optimized CNNs model, covering a 13° × 13° spatial domain, effectively explains over 70% of daily ozone variability, outperforming alternative empirical approaches by 7 to 62%. Model interpretations reveal the crucial roles of 2-m temperature and humidity as primary drivers, contributing 16% and 15% to daily ozone fluctuations, respectively. Regional wind fields account for up to 40% of ozone changes during the episodes. CNNs successfully replicate observed ozone temporal patterns, attributing -5-6 μg·m-3 of interannual ozone variability to weather anomalies. Our interpretable CNNs framework enables quantitative attribution of historical ozone fluctuations to nonlinear meteorological effects across spatiotemporal scales, offering vital process-based insights for managing megacity air quality amidst changing climate regimes | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a convolutional neural networks | |
| 650 | 4 | |a deep learning | |
| 650 | 4 | |a megacity air quality | |
| 650 | 4 | |a meteorological impact | |
| 650 | 4 | |a ozone temporal variability | |
| 650 | 4 | |a surface ozone | |
| 700 | 1 | |a Shen, Huizhong |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Aoxing |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Haitong Zhe |e verfasserin |4 aut | |
| 700 | 1 | |a Zheng, Lianming |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Jianfeng |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Chanfang |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yilin |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Chen |e verfasserin |4 aut | |
| 700 | 1 | |a Ye, Jianhuai |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Lei |e verfasserin |4 aut | |
| 700 | 1 | |a Fu, Tzung-May |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Xin |e verfasserin |4 aut | |
| 700 | 1 | |a Tao, Shu |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Environmental science & technology |d 1967 |g (2024) vom: 14. März |w (DE-627)NLM112374735 |x 1520-5851 |7 nnns |
| 773 | 1 | 8 | |g year:2024 |g day:14 |g month:03 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1021/acs.est.3c07907 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |j 2024 |b 14 |c 03 | ||