ArticleEnvironmental science & technology2020
Deep Learning for Prediction of the Air Quality Response to Emission Changes.
Article in Environmental science & technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed.
- A deep learning model for predicting daily PMScience advances · 2026Article
- Advances in Emissions, Modeling, and Source Apportionment.Environmental science & technology · 2026Article
- A LASSO-based reduced-form CMAQ model for predicting ozone and PM2.5 responses to emission changes in South Korea.PloS one · 2026Article
- Data-driven assessment of air quality and health benefits from future shipping emission controls in coastal China.Eco-Environment & Health · 2025Article
- Conditional UNet emulation of CMAQ simulations for fine particulate matter concentration prediction.Scientific reports · 2025Article
- Air Pollutant Traceability Based on Federated Learning of Edge Intelligent Perception Agents.Sensors (Basel, Switzerland) · 2025Article
- Assessment model of ozone pollution based on SHAP-IPSO-CNN and its application.Scientific reports · 2025Article
- DynamicSeq2SeqXGB for PM2.5 imputation in extremely sparse environmental monitoring networks.PloS one · 2025Article
- Enhancing PM2.5 prediction by mitigating annual data drift using wrapped loss and neural networks.PloS one · 2025Article
- Perspective improvement of regional air pollution burden of disease estimation by machine intelligence.Frontiers in public health · 2025Article
- Identification of response regulation governing ozone formation based on influential factors using a random forest approach.Heliyon · 2024Article
- Air Quality, Health, and Equity Benefits of Carbon Neutrality and Clean Air Pathways in China.Environmental science & technology · 2024Article
- Machine learning approaches reveal highly heterogeneous air quality co-benefits of the energy transition.iScience · 2023Article
- Predicting of Daily PMToxics · 2023Article
- Chang impact analysis of level 3 COVID-19 alert on air pollution indicators using artificial neural network.Ecological informatics · 2022Article
- Flexible Bayesian Ensemble Machine Learning Framework for Predicting Local Ozone Concentrations.Environmental science & technology · 2022Article
- Mimicking atmospheric photochemical modelling with a deep neural network.Atmospheric research · 2022Article
- Predicting the Nonlinear Response of PMAtmosphere · 2021Article
- The Detailed Emissions Scaling, Isolation, and Diagnostic (DESID) module in the Community Multiscale Air Quality (CMAQ) modeling system version 5.3.2.Geoscientific model development · 2021Article
- Data assimilation of ambient concentrations of multiple air pollutants using an emission-concentration response modeling framework.Atmosphere · 2020Article
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
Efficient prediction of the air quality response to emission changes is a prerequisite for an integrated assessment system in developing effective control policies. Yet, representing the nonlinear response of air quality to emission controls with accuracy remains a major barrier in air quality-related decision making. Here, we demonstrate a novel method that combines deep learning approaches with chemical indicators of pollutant formation to quickly estimate the coefficients of air quality response functions using ambient concentrations of 18 chemical indicators simulated with a comprehensive atmospheric chemical transport model (CTM). By requiring only two CTM simulations for model application, the new method significantly enhances the computational efficiency compared to existing methods that achieve lower accuracy despite requiring 20+ CTM simulations (the benchmark statistical model). Our results demonstrate the utility of deep learning approaches for capturing the nonlinearity of atmospheric chemistry and physics and the prospects of the new method to support effective policymaking in other environment systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.