Indoor PM2.5 forecasting and the association with outdoor air pollution: a modelling study based on sensor data in Australia

• URL: http://arxiv.org/abs/2405.07404v1
• Date: Mon, 13 May 2024 00:51:36 GMT
• Title: Indoor PM2.5 forecasting and the association with outdoor air pollution: a modelling study based on sensor data in Australia
• Authors: Wenhua Yu, Bahareh Nakisa, Seng W. Loke, Svetlana Stevanovic, Yuming Guo, Mohammad Naim Rastgoo,
• Abstract summary: This study aims to predict hourly indoor fine particulate matter (PM2.5) concentrations across 24 distinct buildings in Australia. Indoor air quality data were gathered from 91 monitoring sensors in eight Australian cities spanning 2019 to 2022. Outdoor PM2.5 concentrations significantly impacted indoor air quality, particularly evident during events like bushfires.
• Score: 8.864955783255803
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Exposure to poor indoor air quality poses significant health risks, necessitating thorough assessment to mitigate associated dangers. This study aims to predict hourly indoor fine particulate matter (PM2.5) concentrations and investigate their correlation with outdoor PM2.5 levels across 24 distinct buildings in Australia. Indoor air quality data were gathered from 91 monitoring sensors in eight Australian cities spanning 2019 to 2022. Employing an innovative three-stage deep ensemble machine learning framework (DEML), comprising three base models (Support Vector Machine, Random Forest, and eXtreme Gradient Boosting) and two meta-models (Random Forest and Generalized Linear Model), hourly indoor PM2.5 concentrations were predicted. The model's accuracy was evaluated using a rolling windows approach, comparing its performance against three benchmark algorithms (SVM, RF, and XGBoost). Additionally, a correlation analysis assessed the relationship between indoor and outdoor PM2.5 concentrations. Results indicate that the DEML model consistently outperformed benchmark models, achieving an R2 ranging from 0.63 to 0.99 and RMSE from 0.01 to 0.663 mg/m3 for most sensors. Notably, outdoor PM2.5 concentrations significantly impacted indoor air quality, particularly evident during events like bushfires. This study underscores the importance of accurate indoor air quality prediction, crucial for developing location-specific early warning systems and informing effective interventions. By promoting protective behaviors, these efforts contribute to enhanced public health outcomes.

Related papers

• When World Models Dream Wrong: Physical-Conditioned Adversarial Attacks against World Models [54.08784776767683]
  We present Physical-Conditioned World Model Attack (PhysCond-WMA), the first white-box world model attack that perturbs physical-condition channels.<n>PhysCond-WMA induces semantic, logic, or decision-level distortion while preserving perceptual fidelity.
  arXiv  Detail & Related papers  (2026-02-21T07:22:37Z)
• Explainable Admission-Level Predictive Modeling for Prolonged Hospital Stay in Elderly Populations: Challenges in Low- and Middle-Income Countries [65.4286079244589]
  Prolonged length of stay (pLoS) is a significant factor associated with the risk of adverse in-hospital events.<n>We develop and explain a predictive model for pLos using admission-level patient and hospital administrative data.
  arXiv  Detail & Related papers  (2026-01-07T23:35:24Z)
• Forecasting Fails: Unveiling Evasion Attacks in Weather Prediction Models [60.728124907335]
  This work introduces Weather Adaptive Adversarial Perturbation Optimization (WAAPO), a novel framework for generating targeted adversarial perturbations.<n>WAAPO achieves this by incorporating constraints for channel sparsity, spatial localization, and smoothness, ensuring that perturbations remain physically realistic and imperceptible.<n>Our experiments highlight critical vulnerabilities in AI-driven forecasting models, where small perturbations to initial conditions can result in significant deviations.
  arXiv  Detail & Related papers  (2025-12-09T17:20:56Z)
• Synergistic Neural Forecasting of Air Pollution with Stochastic Sampling [50.3911487821783]
  Air pollution remains a leading global health and environmental risk, particularly in regions vulnerable to episodic air pollution spikes due to wildfires, urban haze and dust storms.<n>Here, we present SynCast, a high-resolution neural forecasting model that integrates meteorological and air composition data to improve predictions of both average and extreme pollution levels.
  arXiv  Detail & Related papers  (2025-10-28T01:18:00Z)
• A comparison between geostatistical and machine learning models for spatio-temporal prediction of PM2.5 data [0.0]
  Exposure to high concentrations of PM2.5$ have been linked to increased respiratory and cardiovascular hospital admissions, more emergency department visits and deaths.<n>Traditional air quality monitoring systems provide limited spatial and temporal data.<n>The advent of low-cost sensors has dramatically improved the granularity of air quality data, enabling real-time, high-resolution monitoring.<n>This study exploits the extensive data from PurpleAir sensors to assess and compare the effectiveness of various statistical and machine learning models in producing accurate hourly PM$_2.5$ maps across California.
  arXiv  Detail & Related papers  (2025-09-15T15:32:57Z)
• Air Quality PM2.5 Index Prediction Model Based on CNN-LSTM [0.2796197251957245]
  We propose an air quality PM2.5 index prediction model based on a hybrid CNN-LSTM architecture.<n>The model effectively combines Convolutional Neural Networks (CNN) for local spatial feature extraction and Long Short-Term Memory (LSTM) networks for modeling temporal dependencies in time series data.<n> Experimental results show that the model achieves a root mean square error (RMSE) of 5.236, outperforming traditional time series models in both accuracy and generalization.
  arXiv  Detail & Related papers  (2025-08-15T04:46:25Z)
• Reasoning or Memorization? Unreliable Results of Reinforcement Learning Due to Data Contamination [67.67725938962798]
  Pre-training on massive web-scale corpora leaves Qwen2.5 susceptible to data contamination in widely used benchmarks.<n>We introduce a generator that creates fully clean arithmetic problems of arbitrary length and difficulty, dubbed RandomCalculation.<n>We show that only accurate reward signals yield steady improvements that surpass the base model's performance boundary.
  arXiv  Detail & Related papers  (2025-07-14T17:55:15Z)
• Air Quality Prediction with A Meteorology-Guided Modality-Decoupled Spatio-Temporal Network [47.699409089023696]
  Air quality prediction plays a crucial role in public health and environmental protection. Existing works underestimate the critical role atmospheric conditions in air quality prediction. MDSTNet is an encoder framework explicitly that captures atmosphere-pollution dependencies for prediction. ChinaAirNet is the first dataset combining air quality records with multi-pressure-level meteorological observations.
  arXiv  Detail & Related papers  (2025-04-14T09:18:11Z)
• Solar Active Regions Emergence Prediction Using Long Short-Term Memory Networks [44.99833362998488]
  We develop Long Short-Term Memory (LSTM) models to predict the formation of active regions (ARs) on the solar surface. Time-series datasets of acoustic power and magnetic flux are used to train LSTM models on predicting continuum intensity, 12 hours in advance. These novel machine learning (ML) models are able to capture variations of the acoustic power density associated with upcoming magnetic flux emergence and continuum intensity decrease.
  arXiv  Detail & Related papers  (2024-09-25T23:09:46Z)
• Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region [62.09891513612252]
  We focus on limited-area modeling and train our model specifically for localized region-level downstream tasks. We consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events. Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
  arXiv  Detail & Related papers  (2024-09-11T19:31:56Z)
• A Framework for Scalable Ambient Air Pollution Concentration Estimation [0.0]
  Ambient air pollution remains a critical issue in the United Kingdom, where data on air pollution concentrations form the foundation for interventions aimed at improving air quality. We introduce a data-driven supervised machine learning model framework designed to address temporal and spatial data gaps by filling missing measurements. This approach provides a comprehensive dataset for England throughout 2018 at a 1kmx1km hourly resolution.
  arXiv  Detail & Related papers  (2024-01-16T18:03:07Z)
• Two Scalable Approaches for Burned-Area Mapping Using U-Net and Landsat Imagery [39.91303506884272]
  This study explores two proposed approaches based on the U-Net model for automating and optimizing the burned-area mapping process. Tests based on 195 representative images of the study area show that increasing dataset balance using the AS model yields better performance.
  arXiv  Detail & Related papers  (2023-11-29T05:42:25Z)
• Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling [58.456404022536425]
  State of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs. Here, a generative diffusion architecture is explored for downscaling such global inputs to km-scale, as a cost-effective machine learning alternative. The model is trained to predict 2km data from a regional weather model over Taiwan, conditioned on a 25km global reanalysis.
  arXiv  Detail & Related papers  (2023-09-24T19:57:22Z)
• FuXi: A cascade machine learning forecasting system for 15-day global weather forecast [34.812266901884996]
  FuXi is a cascaded ML weather forecasting system that provides 15-day global forecasts with a temporal resolution of 6 hours and a spatial resolution of 0.25 degree. FuXi has comparable forecast performance to ECMWF EM in 15-day forecasts, making FuXi the first ML-based weather forecasting system to accomplish this achievement.
  arXiv  Detail & Related papers  (2023-06-22T13:34:26Z)
• FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead [93.67314652898547]
  We present FengWu, an advanced data-driven global medium-range weather forecast system based on Artificial Intelligence (AI) FengWu is able to accurately reproduce the atmospheric dynamics and predict the future land and atmosphere states at 37 vertical levels on a 0.25deg latitude-longitude resolution. The results suggest that FengWu can significantly improve the forecast skill and extend the skillful global medium-range weather forecast out to 10.75 days lead.
  arXiv  Detail & Related papers  (2023-04-06T09:16:39Z)
• WF-UNet: Weather Fusion UNet for Precipitation Nowcasting [4.213427823201119]
  We investigate the use of a UNet core-model and its extension for precipitation nowcasting in western Europe for up to 3 hours ahead. We have collected six years of precipitation and wind radar images from Jan 2016 to Dec 2021 of 14 European countries. WF-UNet outperforms the other examined best-performing architectures by 22%, 8% and 6% lower MSE at a horizon of 1, 2 and 3 hours respectively.
  arXiv  Detail & Related papers  (2023-02-08T14:50:52Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)
• Evaluation of Time Series Forecasting Models for Estimation of PM2.5 Levels in Air [0.0]
  The study adopts ARIMA, FBProphet, and deep learning models such as LSTM, 1D CNN, to estimate the concentration of PM2.5 in the environment. Our predicted results convey that all adopted methods give comparative outcomes in terms of average root mean squared error.
  arXiv  Detail & Related papers  (2021-04-07T16:24:39Z)
• Deep-AIR: A Hybrid CNN-LSTM Framework for Air Quality Modeling in Metropolitan Cities [28.233460564726034]
  Deep-AIR is a novel hybrid deep learning framework that combines a convolutional neural network with a long short-term memory network. Our proposed framework creates 1x1 convolution layers to strengthen the learning of cross-feature spatial interaction between air pollution and urban dynamic features. Our model attains an accuracy of 67.6%, 77.2%, and 66.1% in fine-grained hourly estimation, 1-hr, and 24-hr air pollution forecast for Hong Kong, and an accuracy of 65.0%, 75.3%, and 63.5% for Beijing.
  arXiv  Detail & Related papers  (2021-03-25T13:47:56Z)
• A Novel CMAQ-CNN Hybrid Model to Forecast Hourly Surface-Ozone Concentrations Fourteen Days in Advance [0.19573380763700707]
  Currently available numerical modeling systems for air quality predictions can forecast 24 to 48 hours in advance. We develop a modeling system based on a convolutional neural network (CNN) model that is not only fast but covers a temporal period of two weeks with a resolution as small as a single hour for 255 stations. Although the primary purpose of this study is the prediction of hourly ozone concentrations, the system can be extended to various other pollutants.
  arXiv  Detail & Related papers  (2020-08-13T16:02:05Z)
• Analytical Equations based Prediction Approach for PM2.5 using Artificial Neural Network [0.0]
  Particulate Matter (PM2.5) is one of the important particulate pollutants to measure the Air Quality Index (AQI) The conventional instruments used by the air quality monitoring stations to monitor PM2.5 are costly, bulkier, time-consuming, and power-hungry. This article presents analytical equations based prediction approach for PM2.5 using an Artificial Neural Network (ANN)
  arXiv  Detail & Related papers  (2020-02-26T11:39:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.