Conventional air quality monitoring systems, such as gas analysers, are commonly used in many developed and developing countries to monitor air quality. However, these techniques have high costs associated with both installation and maintenance. One possible solution to complement these techniques is the application of low-cost air quality sensors (LAQSs), which have the potential to give higher spatial and temporal data of gas pollutants with high precision and accuracy. In this paper, we present DiracSense, a custom-made LAQS that monitors the gas pollutants ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO). The aim of this study is to investigate its performance based on laboratory calibration and field experiments. Several model calibrations were developed to improve the accuracy and performance of the LAQS. Laboratory calibrations were carried out to determine the zero offset and sensitivities of each sensor. The results showed that the sensor performed with a highly linear correlation with the reference instrument with a response-time range from 0.5 to 1.7 min. The performance of several calibration models including a calibrated simple equation and supervised learning algorithms (adaptive neuro-fuzzy inference system or ANFIS and the multilayer feed-forward perceptron or MLP) were compared. The field calibration focused on O3 measurements due to the lack of a reference instrument for CO and NO2. Combinations of inputs were evaluated during the development of the supervised learning algorithm. The validation results demonstrated that the ANFIS model with four inputs (WE OX, AE OX, T, and NO2) had the lowest error in terms of statistical performance and the highest correlation coefficients with respect to the reference instrument (0.8 < r < 0.95). These results suggest that the ANFIS model is promising as a calibration tool since it has the capability to improve the accuracy and performance of the low-cost electrochemical sensor.

During the COVID-19 pandemic, key policies aimed at reducing exposure to the virus from social distancing, restrictions on travel through to strongly enforced lockdowns. However, COVID-19 restrictions required people to spend more time at home so the exposure to air pollutants shifted to being derived from that of domestic interiors, rather than outdoors or the workplace environment. This study aims to characterise the influence of lockdown intervention on the balance of indoor and outdoor PM2.5 exposure in a Malaysian suburb. We also calculate the potential health risk from exposure to both indoor and outdoor PM2.5 to give context to personal exposure assessment in different microenvironments during the COVID-19 lockdown, known locally as Movement Control Orders (MCO). The implementation of the MCOs slightly reduced daily average of outdoor PM2.5 concentrations (median of 12.63 μg m-3 before and 11.72 μg m-3). In the Malaysian apartment considered here, cooking led to a substantial increase in exposure from increasing concentrations in PM2.5 during a COVID-19 lockdown (maximum average concentration at 52.2 µg m-3). The estimated excess risk to health was about 25% for lung cancer from staying indoor. Thus, there seems a potential for greater exposure to fine particles indoors under lockdown, so it is likely premature to suggest that more lives were saved through a reduction of outdoor pollutants than lost in the pandemic. Unfortunately, little is known about the toxicity of indoor particles and the types of exposures that result where people increase the amount of time they spend working from home or staying indoors, especially during periods of lockdown.

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The restriction of daily and economic-related activities due to COVID-19 pandemic via lockdown order has been reported to improve air quality. This study evaluated temporal and spatial variations of four major air pollutant concentrations across Malaysia before (March 4, 2020-March 17, 2020) and during the implementation of different phases of Movement Control Order (MCO) (March 18, 2020-May 12, 2020) from 65 official regulatory air quality stations. Results showed that restriction in daily and economic activities has remarkably reduced the air quality in all suburban , urban, and industrial settings with relatively small contributions from meteorological conditions. Overall, compared to before MCO, average concentrations of PM 2.5 , CO, and NO 2 reduced by 23.1%, 21.74%, and 54.0%, respectively, while that of SO 2 was constant. The highest reduction of PM 2.5 , CO, and NO 2 were observed in stations located in urban setting, where 63% stations showed significant reduction (p < 0.05) for PM 2.5 and CO, while all stations showed significant reduction in NO 2 concentrations. It was also revealed that 70.5% stations recorded lower concentrations of PM 2.5 during MCO compared to before MCO, despite that high numbers of local hotspots were observed simultaneously from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). Spatial analysis showed that the northern part of Peninsular had the highest significant reduction of PM 2.5 , while the highest of NO 2 and CO reduction were found in stations located in the central region. All pollutants exhibit similar diurnal trends when compared between pre-and during MCO although significant lower readings were observed during MCO. This study gives confidence to regulatory body; the enforcement of strict air pollution prevention and control policies could help in reducing pollution.