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.

Medical imaging is gaining significant attention in healthcare, including breast cancer. Breast cancer is the most common cancer-related death among women worldwide. Currently, histopathology image analysis is the clinical gold standard in cancer diagnosis. However, the manual process of microscopic examination involves laborious work and can be misleading due to human error. Therefore, this study explored the research status and development trends of deep learning on breast cancer image classification using bibliometric analysis. Relevant works of literature were obtained from the Scopus database between 2014 and 2021. The VOSviewer and Bibliometrix tools were used for analysis through various visualization forms. This study is concerned with the annual publication trends, co-authorship networks among countries, authors, and scientific journals. The co-occurrence network of the authors’ keywords was analyzed for potential future directions of the field. Authors started to contribute to publications in 2016, and the research domain has maintained its growth rate since. The United States and China have strong research collaboration strengths. Only a few studies use bibliometric analysis in this research area. This study provides a recent review on this fast-growing field to highlight status and trends using scientific visualization. It is hoped that the findings will assist researchers in identifying and exploring the potential emerging areas in the related field.

Isoprene (C H) plays an important role in the formation of surface ozone (O3) and the secondary organic aerosol (SOA) which contributed to the climate change. This study aims to determine hourly distribution of tropospheric isoprene over the Western Coast of Antarctic Peninsula (WCAP) during the Malaysian Antarctic Scientific Expedition Cruise 2016 (MASEC′16). In-situ measurements of isoprene were taken using a custom-built gas chromatography with

Globally, the COVID-19 pandemic has had both positive and negative impacts on humans and the environment. In general, a positive impact can be seen on the environment, especially in regard to air quality. This positive impact on air quality around the world is a result of movement control orders (MCO) or lockdowns, which were carried out to reduce the cases of COVID-19 around the world. Nevertheless, data on the effects on air quality both during and post lockdown at local scales are still sparse. Here, we investigate changes in air quality during normal days, the MCOs (MCO 1, 2 and 3) and post MCOs, namely the Conditional Movement Control Order (CMCO) and the Recovery Movement Control Order (RMCO) in the Klang Valley region. In this study, we used the air sensor network AiRBOXSense that measures carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2) and particulate matter (PM2.5 and PM10) at Petaling Jaya South (PJS), Kelana Jaya (KJ) and Kota Damansara (KD). The results showed that the daily average concentrations of CO and NO2 mostly decreased in the order of normal days > MCO (MCO 1, 2 and 3) > CMCO > RMCO. PM10, PM2.5, SO2 and O3 showed a decrease from the MCO to RMCO. PJS showed that air pollutant concentrations decreased from normal days to the lockdown phases. This clearly shows the effects of ‘work from home’ orders at all places in the PJS city. The greatest percentage reductions in air pollutants were observed during the change from normal days to MCO 1 (24% to 64%), while during MCO 1 to MCO 2, the concentrations were slightly increased during the changes of the lockdown phase, except for SO2 and NO2 over PJS. In KJ, most of the air pollutants decreased from MCO 1 to MCO 3 except for CO. However, the percentage reduction and increments of the gas pollutants were not consistent during the different phases of lockdown, and this effect was due to the sensor location—only 20 m from the main highway (vehicle emissions). The patterns of air pollutant concentrations over the KD site were similar to the PJS site; however, the percentage reduction and increases of PM2.5, O3, SO2 and CO were not consistent. We believe that local burning was the main contribution to these unstable patterns during the lockdown period. The cause of these different changes in concentrations may be due to the relaxation phases during the lockdown at each station, where most of the common activities, such as commuting and industrial activities changed in frequency from the MCO, CMCO and RMCO. Wind direction also affected the concentrations, for example, during the CMCO and RMCO, most of the pollutants were blowing in from the Southeast region, which mostly consists of a city center and industrial areas. There was a weak correlation between air pollutants and the temperature and relative humidity at all stations. Health risk assessment analysis showed that non-carcinogenic risk health quotient (HQ) values for the pollutants at all stations were less than 1, suggesting unlikely non-carcinogenic effects, except for SO2 (HQ > 1) in KJ. The air quality information showed that reductions in air pollutants can be achieved if traffic and industry emissions are strictly controlled.

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