Observations of ground-level ozone (O 3), nitric oxide (NO), nitrogen dioxide (NO 2), particulate matter (PM 10) and meteorological parameter (temperature, relative humidity and wind speed) fluctuations during high particulate event (HPE) and non-HPE in Malaysia have been conducted for 2 years (2013 and 2014). The study focuses on urban areas, namely, Shah Alam, Petaling Jaya and Bandaraya Melaka. The diurnal variations of ground-level O 3 concentration were higher during HPE than those during non-HPE in all urban areas. The concentration of O 3 fluctuated more in 2014 than 2013 due to the higher incidences of HPE. Temperature and wind speed fluctuated with higher PM 10 , NO 2 and NO concentrations during HPE than those during non-HPE in all urban sites. Relative humidity was lower during HPE than that during non-HPE. Positive correlations were found between PM 10 and ozone during HPE for Shah Alam and Petaling Jaya with 0.81 and 0.79, respectively. Meanwhile, negative correlation (− 0.76) was recorded for Bandaraya Melaka. The non-HPE correlation of PM 10 and O 3 showed negative values for all locations except Petaling Jaya (0.02). Temperature and wind speed shows a strong positive correlation with ozone for all locations during HPE and non-HPE with the highest at Shah Alam (0.97). Inverse relationships were found between relative humidity and O 3 , in which the highest was for Shah Alam (− 0.96) in 2013 and Shah Alam (− 0.97) and Bandaraya Melaka (− 0.97) in 2014. The result of the ozone best-fit equation obtained an R 2 of 0.6730. The study parameters had a significant positive relationship with the ozone predictions during HPE.

Environmental impact assessment (EIA) is a preemptive tool used by engineers, environmental consultants and planners to avoid the most likely adverse consequences of development projects. As a planning tool, EIA should curb the harmful effects from all stages of a project lifecycle. Landslides and flash floods are the most common problems faced by Malaysians almost yearly due to rapid development, especially that involving modification of watercourses, clearing of land and projects on hill slopes. Despite such issues, existing legislation and new guidelines have been enacted by the Malaysian government and must be followed by any proposing project team before starting development projects. The Department may have accepted an EIA report of the project. Still, several developments may have neglected the guidelines, especially during construction involving earthworks and exposure of the surrounding environment, place and people to a high risk of disaster caused by mishaps and accidents. The incidence of landslides and flash floods as reported in newspapers, journals, reports and books since 1919 is explored in this study to determine the details of the losses and locations. Despite the enactment of the new EIA law, landslides and flash floods continue to occur. This situation justifies the need to revise the approach based on sizes and include other factors, namely, the risk indices for disaster to happen and the effectiveness of EIA in reducing disaster risks in projects.

The PM10 prediction has received considerable attention due to its harmful effects on human health. Machine learning approaches have the potential to predict and classify future PM10 concentrations accurately. Therefore, in this study, three machine learning algorithms—namely, decision tree (DT), boosted regression tree (BRT), and random forest (RF)—were applied for the prediction of PM10 in Kota Bharu, Kelantan. The results from these three methods were compared to find the best method to predict PM10 concentration for the next day by using the maximum daily data from January 2002 to December 2017. To this end, 80% of the data were used for training and 20% for validation of the models. The performance measure of the PM10 concentration was based on accuracy, sensitivity, specificity, and precision for RF, BRT, and DT, respectively, which indicates that these three models were developed effectively, and they are applicable in the prediction of other atmospheric environmental data. The best model to use in predicting the next day’s PM10 concentration classification was the random forest classifier, with an accuracy of 98.37, sensitivity of 97.19, specificity of 99.55, and precision of 99.54, but the result of the boosted regression tree was substantially different from the RF model, with an accuracy of 98.12, sensitivity of 97.51, specificity of 98.72, and precision of 98.71. The best model can assist local governments in providing early warnings to people who are at risk of acute and chronic health consequences from air pollution.

Ground-level ozone (O3) is mainly produced during daytime in the presence of ultraviolet (UV) light and later destroyed by nitrogen oxides during nighttime. However, light pollution caused by the excessive use of artificial lights may disrupt the chemistry of night-time ground-level O3 by providing enough energy to initiate nighttime ground-level O3 production. In this study, nighttime (7 p.m. to 7 a.m.) ground-level O3, nitrogen oxide (NO), and nitrogen dioxides (NO2) concentrations were observed for three years (2013, 2014, and 2015). The existence of O3 was found during nighttime, especially in urban areas with a concentration range of 8–20 ppb. The results suggested that nighttime variations of ground-level O3 concentrations were higher in urban areas than in suburban areas. The mean nighttime O3 concentration at urban sites varied, possibly because the distribution of anthropogenic lights around the urban sites is brighter than in suburban locations, as indicated by the data from the light-pollution map. This anthropogenic light has not caused the suspected nighttime photolysis processes, which directly slowed nighttime oxidation. The photochemistry rate of JNO2/k3 was supposed to be near zero because of the absence of photochemical reactions at night. However, the minimum concentration in all urban and suburban sites ranged from 2–3 ppb, indicating that O3 might also form at night, albeit not due to light pollution.

A B S T R A C TParticulate matter (PM), especially those with an aerodynamic particle size of less than 10 μm (PM 10 ), is typically emitted from transboundary forest fires. A large-scale forest fire may contribute to a haze condition known as a high particulate event (HPE), which has affected Southeast Asia, particularly Peninsular Malaysia, for a long time. Such event can alter the photochemical reactions of secondary pollutants. This work investigates the influence of PM on ground-level ozone (O 3 ) formation during HPE. Five continuous air quality monitoring stations from different site categories (i.e., industrial, urban and background) located across Peninsular Malaysia were selected in this study during the HPEs in 2013 and 2014. Result clearly indicated that O 3 concentrations were significantly higher during HPE than during non-HPE in all the sites. The O 3 diurnal variation in each site exhibited a similar pattern, whereas the magnitudes of variation during HPE and non-HPE differed. Light scattering and atmospheric attenuation were proven to be associated with HPE, which possibly affected O 3 photochemical reactions during HPE. Critical conversion time was used as the main determining factor when comparing HPE and non-HPE conditions. A possible screening effect that resulted in the shifting of the critical transformation point caused a delay of approximately of 15-30 min. The shifting was possibly influenced by the attenuation of sunlight in the morning during HPE. A negative correlation between O 3 and PM 10 was observed during the HPE in Klang in 2013 and 2014, with −0.87. Essentially, HPE with a high PM concentration altered ground-level O 3 formation.