Assessment of Malaysia-wide PM2.5 Forecasts from a Global Model

Abstract Airborne particulate matter with an aerodynamic diameter of less than 2.5 µm (PM2.5) is a major air pollutant worldwide. In Malaysia, transboundary ‘haze’ episodes with elevated PM2.5 concentrations linked to fires are common, causing health and economic harms. To reduce impacts, forecastin...

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Main Authors: Zhixian Tan, Mohd Talib Latif, Matthew J. Ashfold
Format: Article
Language:English
Published: Springer 2023-06-01
Series:Aerosol and Air Quality Research
Subjects:
Online Access:https://doi.org/10.4209/aaqr.220444
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author Zhixian Tan
Mohd Talib Latif
Matthew J. Ashfold
author_facet Zhixian Tan
Mohd Talib Latif
Matthew J. Ashfold
author_sort Zhixian Tan
collection DOAJ
description Abstract Airborne particulate matter with an aerodynamic diameter of less than 2.5 µm (PM2.5) is a major air pollutant worldwide. In Malaysia, transboundary ‘haze’ episodes with elevated PM2.5 concentrations linked to fires are common, causing health and economic harms. To reduce impacts, forecasting PM2.5 can enable effective PM2.5 management and decision-making. Until now, PM2.5 forecasts via a global mechanistic chemical transport model (CTM) have not been evaluated in the setting of Malaysia, where operational PM2.5 forecasting systems for preventive warnings are not yet deployed. Hence, this study aims to evaluate the performance of PM2.5 forecasts produced by a global CTM and to assess their suitability for use nation-wide in Malaysia. We used the surface PM2.5 forecasts from the Copernicus Atmosphere Monitoring Service’s (CAMS) global atmospheric composition forecast dataset (CAMS-GACF) and evaluated them against hourly PM2.5 observations recorded throughout Malaysia from 2018 to 2020 via exceedance and accuracy analyses. We found that cycle 46r1 CAMS-GACF performance in Malaysia was generally weaker (critical success index (CSI) = 31%, R2 = 0.36) than reported in other studies (CSI = 20–54%, R2 = 0.32–0.79) focused on other countries, across multiple metrics in both analyses. We found CAMS-GACF did not accurately capture local-scale spatiotemporal variations in PM2.5 spatially and diurnally. However, we found CAMS-GACF captured better the increased regional PM2.5 pollution during the transboundary ‘haze’ episode of 2019. Based on our findings, we also propose recommendations on integrating CAMS-GACF in early-warning systems in Malaysia and on improving forecasts via bias-correction.
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spelling doaj-art-96e0910e93e042a69e41520800bf97cb2025-02-09T12:23:19ZengSpringerAerosol and Air Quality Research1680-85842071-14092023-06-0123911910.4209/aaqr.220444Assessment of Malaysia-wide PM2.5 Forecasts from a Global ModelZhixian Tan0Mohd Talib Latif1Matthew J. Ashfold2School of Environmental and Geographical Sciences, University of Nottingham MalaysiaDepartment of Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaan MalaysiaSchool of Environmental and Geographical Sciences, University of Nottingham MalaysiaAbstract Airborne particulate matter with an aerodynamic diameter of less than 2.5 µm (PM2.5) is a major air pollutant worldwide. In Malaysia, transboundary ‘haze’ episodes with elevated PM2.5 concentrations linked to fires are common, causing health and economic harms. To reduce impacts, forecasting PM2.5 can enable effective PM2.5 management and decision-making. Until now, PM2.5 forecasts via a global mechanistic chemical transport model (CTM) have not been evaluated in the setting of Malaysia, where operational PM2.5 forecasting systems for preventive warnings are not yet deployed. Hence, this study aims to evaluate the performance of PM2.5 forecasts produced by a global CTM and to assess their suitability for use nation-wide in Malaysia. We used the surface PM2.5 forecasts from the Copernicus Atmosphere Monitoring Service’s (CAMS) global atmospheric composition forecast dataset (CAMS-GACF) and evaluated them against hourly PM2.5 observations recorded throughout Malaysia from 2018 to 2020 via exceedance and accuracy analyses. We found that cycle 46r1 CAMS-GACF performance in Malaysia was generally weaker (critical success index (CSI) = 31%, R2 = 0.36) than reported in other studies (CSI = 20–54%, R2 = 0.32–0.79) focused on other countries, across multiple metrics in both analyses. We found CAMS-GACF did not accurately capture local-scale spatiotemporal variations in PM2.5 spatially and diurnally. However, we found CAMS-GACF captured better the increased regional PM2.5 pollution during the transboundary ‘haze’ episode of 2019. Based on our findings, we also propose recommendations on integrating CAMS-GACF in early-warning systems in Malaysia and on improving forecasts via bias-correction.https://doi.org/10.4209/aaqr.220444CAMSIFSEarly warningsHaze episodesGround-level air quality monitoring
spellingShingle Zhixian Tan
Mohd Talib Latif
Matthew J. Ashfold
Assessment of Malaysia-wide PM2.5 Forecasts from a Global Model
Aerosol and Air Quality Research
CAMS
IFS
Early warnings
Haze episodes
Ground-level air quality monitoring
title Assessment of Malaysia-wide PM2.5 Forecasts from a Global Model
title_full Assessment of Malaysia-wide PM2.5 Forecasts from a Global Model
title_fullStr Assessment of Malaysia-wide PM2.5 Forecasts from a Global Model
title_full_unstemmed Assessment of Malaysia-wide PM2.5 Forecasts from a Global Model
title_short Assessment of Malaysia-wide PM2.5 Forecasts from a Global Model
title_sort assessment of malaysia wide pm2 5 forecasts from a global model
topic CAMS
IFS
Early warnings
Haze episodes
Ground-level air quality monitoring
url https://doi.org/10.4209/aaqr.220444
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