Coastal meteorology on the dispersion of air particles at the Bachok GAW Station

Quantifying the impacts of Canadian wildfires on regional air pollution networks

Wildfire smoke greatly impacts regional atmospheric systems, causing changes in the behavior of pollution. However, the impacts of wildfire smoke on pollution behavior are not easily quantifiable due to the complex nature of atmospheric systems. Air pollution correlation networks have been used to quantify air pollution behavior during ambient conditions. However, it is unknown how extreme pollution events impact these networks. Therefore, we propose a multidimensional air pollution correlation network framework to quantify the impacts of wildfires on air pollution behavior. The impacts are quantified by comparing two time periods, one during the 2023 Canadian wildfires and one during normal conditions with two complex network types for each period. In this study, the value network represents PM2.5 concentrations and the rate network represents the rate of change of PM2.5 concentrations. Wildfires' impacts on air pollution behavior are captured by structural changes in the networks. The wildfires caused a discontinuous phase transition during percolation in both network types which represents non-random organization of the most significant spatiotemporal correlations. Additionally, wildfires caused changes to the connectivity of stations leading to more interconnected networks with different influential stations. During the wildfire period, highly polluted areas are more likely to form connections in the network, quantified by an 86 % and 19 % increase in the connectivity of the value and rate networks respectively compared to the normal period. In this study, we create novel understandings of the impacts of wildfires on air pollution correlation networks, show how our method can create important insights into air pollution patterns, and discuss potential applications of our methodologies. This study aims to enhance capabilities for wildfire smoke exposure mitigation and response strategies.

Atmospheric microplastic transport and deposition to urban and pristine tropical locations in Southeast Asia

Atmospheric microplastic transport is an important delivery pathway with the deposition of microplastics to ecologically important regions raising environmental concerns. Investigating atmospheric delivery pathways and their deposition rates in different ecosystems is necessary to understanding its global impact. In this study, atmospheric deposition was collected at three sites in Malaysia, two urban and one pristine, covering the Northeast and Southwest monsoons to quantify the role of this pathway in Southeast Asia. Air mass back trajectories showed long-range atmospheric transport of microplastics to all sites with atmospheric deposition varying from 114 to 689 MP/m2/day. For the east coast of Peninsular Malaysia, monsoonal season influenced microplastic transport and deposition rate with peak microplastic deposition during the Northeast monsoon due to higher wind speed. MP morphology combined with size fractionation and plastic type at the coastal sites indicated a role for long-range marine transport of MPs that subsequently provided a local marine source to the atmosphere at the coastal sites.

Exposure models for particulate matter elemental concentrations in Southern California

Due to a scarcity of routine monitoring of speciated particulate matter (PM), there has been limited capability to develop exposure models that robustly estimate component-specific concentrations. This paper presents the largest such study conducted in a single urban area. Using samples that were collected at 220 locations over two seasons, quasi-ultrafine (PM_0.2), accumulation mode fine (PM_0.2-2.5), and coarse (PM_2.5-10) particulate matter concentrations were used to develop spatiotemporal regression, machine learning models that enabled predictions of 24 elemental components in eight Southern California communities. We used supervised variable selection of over 150 variables, largely from publicly available sources, including meteorological, roadway and traffic characteristics, land use, and dispersion model estimates of traffic emissions. PM components that have high oxidative potential (and potentially large health effects) or are otherwise important markers for major PM sources were the primary focus. We present results for copper, iron, and zinc (as non-tailpipe vehicle emissions); elemental carbon (diesel emissions); vanadium (ship emissions); calcium (soil dust); and sodium (sea salt). Spatiotemporal linear regression models with 17 to 36 predictor variables including meteorology; distance to different classifications of roads; intersections and off ramps within a given buffer distance; truck and vehicle traffic volumes; and near-roadway dispersion model estimates produced superior predictions over the machine learning approaches (cross validation R-squares ranged from 0.76 to 0.92). Our models are easily interpretable and appear to have more effectively captured spatial gradients in the metallic portion of PM than other comparably large studies, particularly near roadways for the non-tailpipe emissions. Furthermore, we demonstrated the importance of including spatiotemporally resolved meteorology in our models as it helped to provide key insights into spatial patterns and allowed us to make temporal predictions.

Ambient Levels, Emission Sources and Health Effect of PM2.5-Bound Carbonaceous Particles and Polycyclic Aromatic Hydrocarbons in the City of Kuala Lumpur, Malaysia

With increasing interest in understanding the contribution of secondary organic aerosol (SOA) to particulate air pollution in urban areas, an exploratory study was carried out to determine levels of carbonaceous aerosols and polycyclic aromatic hydrocarbons (PAHs) in the city of Kuala Lumpur, Malaysia. PM2.5 samples were collected using a high-volume sampler for 24 h in several areas in Kuala Lumpur during the north-easterly monsoon from January to March 2019. Samples were analyzed for water-soluble organic carbon (WSOC), organic carbon (OC), and elemental carbon (EC). Secondary organic carbon (SOC) in PM2.5 was estimated. Particle-bound PAHs were analyzed using gas chromatography-flame ionization detector (GC-FID). Average concentrations of WSOC, OC, and EC were 2.73 ± 2.17 (range of 0.63–9.12) µg/m3, 6.88 ± 4.94 (3.12–24.1) µg/m3, and 3.68 ± 1.58 (1.33–6.82) µg/m3, respectively, with estimated average SOC of 2.33 µg/m3, contributing 34% to total OC. The dominance of char-EC over soot-EC suggests that PM2.5 is influenced by biomass and coal combustion sources. The average of total PAHs was 1.74 ± 2.68 ng/m3. Source identification methods revealed natural gas and biomass burning, and urban traffic combustion as dominant sources of PAHs in Kuala Lumpur. A deterministic health risk assessment of PAHs was conducted for several age groups, including infant, toddler, children, adolescent, and adult. Carcinogenic and non-carcinogenic risk of PAH species were well below the acceptable levels recommended by the USEPA. Backward trajectory analysis revealed north-east air mass brought pollutants to the studied areas, suggesting the north-easterly monsoon as a major contributor to increased air pollution in Kuala Lumpur. Further work is needed using long-term monitoring data to understand the origin of PAHs contributing to SOA formation and to apply source-risk apportionment to better elucidate the potential risk factors posed by the various sources in urban areas in Kuala Lumpur.