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High nighttime ozone (O 3 ) concentration levels were observed in Kemaman, Terengganu, and results were compared with those in other places in Malaysia. In this study, the contribution of precursors [nitric oxide (NO) and nitrogen dioxide (NO 2 )] and meteorological factors wind speed, and wind direction) toward long-term high nighttime O 3 over the period of 1999 to 2010 was evaluated. During this period, the recorded highest nighttime O 3 ground level was 89 ppb with more than 25% surpassing 20 ppb. Analysis shows that minimal decreasing trends were measured in Kemaman. Lower nitrogen oxide (NO x ) concentrations restricted the sinking agents; thus, reducing the depletion rates allowed O 3 to remain in the atmosphere. Minimal associations were observed between the O 3 concentration level and the speed and direction of wind. Accordingly, the largest contributor toward high nighttime O 3 ground level concentration in Kemaman was most probably NO x concentration.

No abstract

Atmospheric particulate matter (PM) has major threats to global health, especially in urban regions around the world. Dhaka, Narayanganj and Gazipur of Bangladesh are positioned as top ranking polluted metropolitan cities in the world. This study assessed the performance of the application of hybrid models, that is, Autoregressive Integrated Moving Average (ARIMA)-Artificial Neural Network (ANN), ARIMA-Support Vector Machine (SVM) and Principle Component Regression (PCR) along with Decision Tree (DT) and CatBoost deep learning model to predict the ambient PM2.5 concentrations. The data from January 2013 to May 2019 with 2342 observations were utilized in this study. Eighty percent of the data was used as training and the rest of the dataset was employed as testing. The performance of the models was evaluated by R2, RMSE and MAE value. Among the models, CatBoost performed best for predicting PM2.5 for all the stations. The RMSE values during the test period were 12.39 µg m−3, 13.06 µg m−3 and 12.97 µg m−3 for Dhaka, Narayanganj and Gazipur, respectively. Nonetheless, the ARIMA-ANN and DT methods also provided acceptable results. The study suggests adopting deep learning models for predicting atmospheric PM2.5 in Bangladesh.

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.

Critical conversion point (CCP) is a very crucial step in production of the ground level O 3 chemistry. Thus, a multivariate analysis was applied on the dataset of nine selected locations in Malaysia from 1999 to 2010. It incorporated hierarchical agglomerative cluster analysis (HACA) to explore the spatial variability of CCP and principal component analysis (PCA) to determine the major sources of the air pollutants that influence ozone CCP. High variability in CCP was observed between the monitoring stations that occurred during critical conversion time (CCT) from 8:00 a.m. to 11:00 a.m. The HACA results grouped the nine monitoring stations into three different clusters, based on the characteristics of ozone concentrations during CCT period. Results of PCA for the three clusters showed that the contributions to O 3 level variation during CCT by meteorological variables (UVB, temperature, relative humidity, and wind speed) are higher at 51.6%, 48.5%, and 33.3% than that of primary air pollutants (NO 2 , SO 2 , PM 10 ) at 19.2%, 21.4%, and 15.2% for cluster 1, cluster 2, and cluster 3, respectively. Therefore, applying a targeted spatial control strategy for ground level O 3 precursors during the CCT period is a crucial step.

This study focused on O 3 variations and the titration effects of NO x during nighttime at urban, industrial, sub-urban and background sites. Nighttime O 3 concentration variations and the presence of high particles with an aerodynamic diameter of less than 10 μm (PM 10 ) were examined because haze disturbs the photochemical reactions of O 3 . Hourly data on O 3 , NO 2 , NO and PM 10 concentrations provided by the Air Quality Division of the Department of Environment were divided into two groups of daytime and nighttime and analysed. The maximum O 3 concentrations during daytime were generally observed during noon. At nighttime, the concentration of O 3 decreased, indicating that destruction activities occurred mainly via titration. The retention of O 3 during daytime caused the nighttime O 3 during haze events to be higher than that during normal days. Apparent fluctuations in nighttime O 3 concentrations were observed in the urban site (20 ± 13 ppb) during haze events. The NO 2 /NO ratio in the urban site during haze was higher than that on normal days; amongst the sites, the urban one had the highest value (6.6). Results indicated that during haze, the reactions between NO and O 3 were enhanced at nighttime, leading to low nighttime NO concentrations. The low nighttime NO concentrations led to low nighttime NO titration rates, which enabled O 3 to persist in ambient air. Nighttime O 3 was not completely absent due to anthropogenic sources. This condition accelerated NO titration to NO 2 , thus promoting O 3 production even during haze.

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.