The world is currently going through the COVID-19 pandemic which has caused hundreds of thousands of deaths in just a few months. Considering the need for lockdown measures, most countries, including Malaysia, have implemented 'Movement Control Orders' (MCOs) as a prevention step to reduce the deadly spread of this disease. Local and worldwide media have reported the immediate improvement of air quality due to this event. Nevertheless, data on the effects of MCOs on air quality at local scales are still sparse. Here, we investigate changes in air quality during the MCO at an urban area using the air sensor network AiRBOXSense which measures monoxide (CO) and particulate matter (PM 2.5 and PM 10). In this study, air pollutant data during normal days were compared with MCO days using a reference analyser and AiRBOXSense. The results showed that the levels of the measured pollutants dropped by ~20 to 60% during the MCO days at most locations. However, CO in Kota Damansara (KD) dropped to 48.7%, but PM 2.5 and PM 10 increased up to 60% and 9.7% respectively during MCO days. Local burning activities in the residential area of KD are believed to be the main cause of the increased PM levels. This study has proven that air pollutant levels have significantly fallen due to the MCO. This air quality level information showed that the reduction of air pollutants can be achieved if traffic and industry emissions are strictly controlled.

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Urban air quality has been deteriorating over time. Pollutant distribution levels in the urban environment may be associated with anthropogenic sources and meteorological conditions. The aim of this study is to determine the variation in concentrations of major air pollutants: carbon monoxide (CO), ozone (O 3), nitrogen dioxide (NO 2), sulphur dioxide (SO 2) and particulate matter (PM 10), with corresponding seasonal variation in a Malaysian urban environment. Eleven years of data from four selected stations, namely Klang (S1), Petaling Jaya (S2), Shah Alam (S3) and Cheras (S4), were analysed for temporal trend variations (yearly and monthly). Statistical analysis using Openair, an R package open source software, has been conducted to assess pollutants in relation to meteorological conditions. Gas concentrations showed little variation between the study sites apart from NO 2 , which recorded its highest concentrations at an industrial site, between 23 and 40 ppb, and is associated with industrial and vehicle emissions. Pollutants that show seasonal variations and frequently exceed the Malaysia Ambient Air Quality Standard (MAAQS) and the National Ambient Air Quality Standard (NAAQS) are O 3 and PM 10 , predominantly related to the monsoon seasons. High levels of O 3 during the northeast monsoon (January-March) are associated with high levels of the precursors of O 3. The concentration of PM 10 associated with tropical biomass burning during southwest monsoon. Shipping emissions and power stations are main contributors for higher level of SO 2. This study shows regional and local factors contribute to the different type of air pollutant concentrations in urban environment.

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Particulate matter (PM) is one of the major pollutants emitted by vehicles that adversely affect human health and the environment. This study evaluates and predicts concentrations and dispersion patterns of PM10 and PM2.5 in Kuala Lumpur city centre. The OML-Highway model calculates hourly time series of PM10 and PM2.5 concentrations and distribution caused by traffic emissions under different scenarios; business as usual (BAU) and 30% traffic reduction to see the impact of traffic reduction for sustainable traffic management. Continuous PM10 and PM2.5 data from a nearby monitoring station were analysed for the year 2019 and compared with modelled concentrations. Annual average concentration at various locations of interest for PM10 and PM2.5 during BAU runs were in the ranges 41.4–65.9 µg/m3 and 30.4–43.7 µg/m3 respectively, compared to during the 30% traffic reduction run ranging at 40.5–59.5 µg/m3 and 29.9–40.3 µg/m3 respectively. The average concentration of PM10 and PM2.5 at the Continuous Air Quality Monitoring Station (CAQMS) was 36.4 µg/m3 and 28.2 µg/m3 respectively. Strong correlations were observed between the predicted and observed data for PM10 and PM2.5 in both scenarios (p < 0.05). This research demonstrated that the reduction of traffic volume in the city contributes to reducing the concentration of particulate matter pollution.

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