Impact of lockdown due to COVID-19 on aerosols and pollutants over Southeast Asia • Reduction in Himawari-8 AOD at urban areas is not affected by seasonal biomass burning • Large reductions (~27%-34%) of tropospheric NO 2 over urban agglomerations • Reductions in PM 10 , PM 2.5 , NO 2 , SO 2 , and CO are 26-31%, 23-32%, 63-64%, 9-20%, and 25-31%, respectively, in Malaysia (urban)

The insufficient number of ground-based stations for measuring Particulate Matter < 10 μm (PM 10) in the developing countries hinders PM 10 monitoring at a regional scale. The present study aims to develop empirical models for PM 10 estimation from space over Malaysia using aerosol optical depth (AOD 550) and meteorological (surface temperature, relative humidity and atmospheric stability) data (retrieved or estimated) from Moderate Resolution Imaging Spectroradiometer (MODIS) during the period 2007-2011. The MODIS retrievals are found to be satisfactorily correlated with ground-based measurements at Malaysia. Multiple linear regressions (MLR) and Artificial Neural Network (ANN) techniques are utilized to develop the empirical models for PM 10 estimation. The model development and training are performed via comparison with measured PM 10 at 29 stations over Malaysia and reveal that the ANN provides slightly higher accuracy with R 2 = 0.71 and RMSE = 11.61 μg m − 3 compared to the MLR method (R 2 = 0.66 and RMSE = 12.39 μg m − 3). Stepwise regression analysis performed on the MLR method reveals that the MODIS AOD 550 is the most important parameter for PM 10 estimations (R 2 = 0.59 and RMSE = 13.61 μg m − 3); however, the inclusion of the meteorological parameters in the MLR increases the accuracy of the retrievals (R 2 = 0.66, RMSE = 12.39 μg m − 3). The estimated PM 10 concentrations are finally validated against surface measurements at 16 stations resulting in similar performance from the ANN model (R 2 = 0.58, RMSE = 10.16 μg m − 3) and MLR technique (R 2 = 0.56, RMSE = 10.58 μg m − 3). The significant accuracy that has been attained in PM 10 estimations from space allows us to assess the pollution levels in Malaysia and map the PM 10 distribution at large spatial and temporal scales.

No abstract

Southeast Asia (SEA) is a hotspot region for atmospheric pollution and haze conditions, due to extensive forest, agricultural and peat fires. This study aims to estimate the PM2.5 concentrations across Malaysia using machine-learning (ML) models like Random Forest (RF) and Support Vector Regression (SVR), based on satellite AOD (aerosol optical depth) observations, ground measured air pollutants (NO2, SO2, CO, O3) and meteorological parameters (air temperature, relative humidity, wind speed and direction). The estimated PM2.5 concentrations for a two-year period (2018–2019) are evaluated against measurements performed at 65 air-quality monitoring stations located at urban, industrial, suburban and rural sites. PM2.5 concentrations varied widely between the stations, with higher values (mean of 24.2 ± 21.6 µg m−3) at urban/industrial stations and lower (mean of 21.3 ± 18.4 µg m−3) at suburban/rural sites. Furthermore, pronounced seasonal variability in PM2.5 is recorded across Malaysia, with highest concentrations during the dry season (June–September). Seven models were developed for PM2.5 predictions, i.e., separately for urban/industrial and suburban/rural sites, for the four dominant seasons (dry, wet and two inter-monsoon), and an overall model, which displayed accuracies in the order of R2 = 0.46–0.76. The validation analysis reveals that the RF model (R2 = 0.53–0.76) exhibits slightly better performance than SVR, except for the overall model. This is the first study conducted in Malaysia for PM2.5 estimations at a national scale combining satellite aerosol retrievals with ground-based pollutants, meteorological factors and ML techniques. The satisfactory prediction of PM2.5 concentrations across Malaysia allows a continuous monitoring of the pollution levels at remote areas with absence of measurement networks.

Abstract. Air pollution is a serious environmental and health issue in Malaysia due to the recent urbanization processes. The main sources of air pollutants are motorized vehicles in urban areas and airports and industrial activities. At the airports, NO2 is the main pollutant of concern besides aerosols particles, yet gap in data availability prevent studies to describe their patterns and quantify their effects on human health and climate change. In this study NO2 data from TROPOMI sensor on board Sentinel 5-P satellite was used to characterize the spatial and temporal patterns of NO2 tropospheric column amounts at major airports in Malaysia. The results demonstrate that NO2 amounts from aircrafts and ground traffic activities are generally higher and/or similar to the amounts found in urban areas. Total tropospheric column amounts of NO2 during the movement restriction imposed due to Covid-19 pandemic between March and April 2020 was approximately 50% lower the total emission during the same period in 2019 (representing a business as usual period). Assessing the spatial pattern and temporal variations in NO2 (both surface and total vertical profile) is important for monitoring the impact of air pollutants on climate change and human health in Malaysia.