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.

Black carbon (BC) is of concern due to its contribution to poor air quality and its adverse effects human health. We carried out the first real-time monitoring of BC in Malaysia using an AE33 Aethalometer. Measurements were conducted between 1 January and 31 May 2020 in a university area in a suburban location of the Klang Valley. The measurement period coincided with the implementation of a movement control order (MCO) in response to COVID-19. The mean concentration of BC before the MCO was 2.34 µg/m³ which decreased by 38% to 1.45 µg/m³ during the MCO. The BC is dominated by fossil-fuel sources (mean proportion BC_ff = 79%). During the MCO, the BC_ff concentration decreased by more than the BC_bb concentration derived from biomass burning. BC and BC_ff show very strong diurnal cycles, which also show some weekday–weekend differences, with maxima during the night and just before noon, and minima in the afternoon. These patterns indicate strong influences on concentrations from both traffic emissions and boundary layer depth. BC was strongly correlated with NO₂ (R = 0.71), another marker of traffic emission, but less strongly with PM_2.5 (R = 0.52). The BC absorption Ångström exponent (AAE) ranged between 1.1 and 1.6. We observed pronounced diurnal cycles of lower AAE in daytime, corresponding to BC_ff contributions from traffic. Average AAE also showed a pronounced increase during the MCO. Our data provides a new reference for BC in suburban Malaysia for the public and policy-makers, and a baseline for future measurements.