Spatial-temporal variations in surface ozone over Ushuaia and the Antarctic region: observations from in situ measurements, satellite data, and global models.

The Antarctic continent is known to be an unpopulated region due to its extreme weather and climate conditions. However, the air quality over this continent can be affected by long-lived anthropogenic pollutants from the mainland. The Argentinian region of Ushuaia is often the main source area of accumulated hazardous gases over the Antarctic Peninsula. The main objective of this study is to report the first in situ observations yet known of surface ozone (O3) over Ushuaia, the Drake Passage, and Coastal Antarctic Peninsula (CAP) on board the RV Australis during the Malaysian Antarctic Scientific Expedition Cruise 2016 (MASEC'16). Hourly O3 data was measured continuously for 23 days using an EcoTech O3 analyzer. To understand more about the distribution of surface O3 over the Antarctic, we present the spatial and temporal of surface O3 of long-term data (2009-2015) obtained online from the World Meteorology Organization of World Data Centre for greenhouse gases (WMO WDCGG). Furthermore, surface O3 satellite data from the free online NOAA-Atmospheric Infrared Sounder (AIRS) database and online data assimilation from the European Centre for Medium-Range Weather Forecasts (ECMWF)-Monitoring Atmospheric Composition and Climate (MACC) were used. The data from both online products are compared to document the data sets and to give an indication of its quality towards in situ data. Finally, we used past carbon monoxide (CO) data as a proxy of surface O3 formation over Ushuaia and the Antarctic region. Our key findings were that the surface O3 mixing ratio during MASEC'16 increased from a minimum of 5 ppb to ~ 10-13 ppb approaching the Drake Passage and the Coastal Antarctic Peninsula (CAP) region. The anthropogenic and biogenic O3 precursors from Ushuaia and the marine region influenced the mixing ratio of surface O3 over the Drake Passage and CAP region. The past data from WDCGG showed that the annual O3 cycle has a maximum during the winter of 30 to 35 ppb between June and August and a minimum during the summer (January to February) of 10 to 20 ppb. The surface O3 mixing ratio during the summer was controlled by photochemical processes in the presence of sunlight, leading to the depletion process. During the winter, the photochemical production of surface O3 was more dominant. The NOAA-AIRS and ECMWF-MACC analysis agreed well with the MASEC'16 data but twice were higher during the expedition period. Finally, the CO past data showed the surface O3 mixing ratio was influenced by the CO mixing ratio over both the Ushuaia and Antarctic regions. Peak surface O3 and CO hourly mixing ratios reached up to ~ 38 ppb (O3) and ~ 500 ppb (CO) over Ushuaia. High CO over Ushuaia led to the depletion process of surface O3 over the region. Monthly CO mixing ratio over Antarctic (South Pole) were low, leading to the production of surface O3 over the Antarctic region.

Source apportionment and health risk assessment among specific age groups during haze and non-haze episodes in Kuala Lumpur, Malaysia.

This study aims to determine PM2.5 concentrations and their composition during haze and non-haze episodes in Kuala Lumpur. In order to investigate the origin of the measured air masses, the Numerical Atmospheric-dispersion Modelling Environment (NAME) and Global Fire Assimilation System (GFAS) were applied. Source apportionment of PM2.5 was determined using Positive Matrix Factorization (PMF). The carcinogenic and non-carcinogenic health risks were estimated using the United State Environmental Protection Agency (USEPA) method. PM2.5 samples were collected from the centre of the city using a high-volume air sampler (HVS). The results showed that the mean PM2.5 concentrations collected during pre-haze, haze and post-haze periods were 24.5±12.0µgm-3, 72.3±38.0µgm-3 and 14.3±3.58µgm-3, respectively. The highest concentration of PM2.5 during haze episode was five times higher than World Health Organisation (WHO) guidelines. Inorganic compositions of PM2.5, including trace elements and water soluble ions were determined using inductively coupled plasma-mass spectrometry (ICP-MS) and ion chromatography (IC), respectively. The major trace elements identified were K, Al, Ca, Mg and Fe which accounted for approximately 93%, 91% and 92% of the overall metals' portions recorded during pre-haze, haze and post-haze periods, respectively. For water-soluble ions, secondary inorganic aerosols (SO42-, NO3- and NH4+) contributed around 12%, 43% and 16% of the overall PM2.5 mass during pre-haze, haze and post-haze periods, respectively. During haze periods, the predominant source identified using PMF was secondary inorganic aerosol (SIA) and biomass burning where the NAME simulations indicate the importance of fires in Sumatra, Indonesia. The main source during pre-haze and post-haze were mix SIA and road dust as well as mineral dust, respectively. The highest non-carcinogenic health risk during haze episode was estimated among the infant group (HI=1.06) while the highest carcinogenic health risk was estimated among the adult group (2.27×10-5).

Characterization of rainwater chemical composition after a Southeast Asia haze event: insight of transboundary pollutant transport during the northeast monsoon.

Open biomass burning in Peninsula Malaysia, Sumatra, and parts of the Indochinese region is a major source of transboundary haze pollution in the Southeast Asia. To study the influence of haze on rainwater chemistry, a short-term investigation was carried out during the occurrence of a severe haze episode from March to April 2014. Rainwater samples were collected after a prolonged drought and analyzed for heavy metals and major ion concentrations using inductively coupled plasma mass spectroscopy (ICP-MS) and ion chromatography (IC), respectively. The chemical composition and morphology of the solid particulates suspended in rainwater were examined using a scanning electron microscope coupled with energy-dispersive X-ray spectroscopy (SEM-EDS). The dataset was further interpreted using enrichment factors (EF), statistical analysis, and a back trajectory (BT) model to find the possible sources of the particulates and pollutants. The results show a drop in rainwater pH from near neutral (pH 6.54) to acidic (