The low-cost and easy-to-use nature of rapidly developed PM_2.5 sensors provide an opportunity to bring breakthroughs in PM_2.5 research to resource-limited countries in Southeast Asia (SEA). This review provides an evaluation of the currently available literature and identifies research priorities in applying low-cost sensors (LCS) in PM_2.5 environmental and health research in SEA. The research priority is an outcome of a series of participatory workshops under the umbrella of the International Global Atmospheric Chemistry Project–Monsoon Asia and Oceania Networking Group (IGAC–MANGO). A literature review and research prioritization are conducted with a transdisciplinary perspective of providing useful scientific evidence in assisting authorities in formulating targeted strategies to reduce severe PM_2.5 pollution and health risks in this region. The PM_2.5 research gaps that could be filled by LCS application are identified in five categories: source evaluation, especially for the distinctive sources in the SEA countries; hot spot investigation; peak exposure assessment; exposure–health evaluation on acute health impacts; and short-term standards. The affordability of LCS, methodology transferability, international collaboration, and stakeholder engagement are keys to success in such transdisciplinary PM_2.5 research. Unique contributions to the international science community and challenges with LCS application in PM_2.5 research in SEA are also discussed.

Traditional medicine is currently being explored and developed widely by various countries because its cost effectiveness and reduced side effects. The green betel (Piper betle Linn.) and the red betel (Piper crocatum Ruiz and Pav.) are plants from the same Piper genus that are widely cultivated in Indonesia and have various phytochemical properties and pharmacological effects. The objective of this research is to determine the cytotoxicity of green leaves and red betel leaves. This research was conducted in two main stages: first, the extraction and preparation through the soxhletation method; second, a cytotoxicity test through the brine shrimp (Artemia salina Leach) lethality test (BSLT). The results showed that there were lethal concentration 50 % (LC50) value in both green betel leaves ethanolic extract and red betel leaves ethanolic extract, with 44.975 μg per mL and 31.556 μg per mL, respectively. The red betel (leaves ethanolic extract has a higher cytotoxicity than green betel (leaves ethanolic extract which can be seen from the lower lethal concentration of 50% (LC50) value. The difference in cytotoxicity is due to differences in plant varieties that cause variations in phytochemical content which affects the pharmacological effects. The green betel leaves ethanolic extract and the red betel leaves ethanolic extract are cytotoxic and have potential anticancer properties.

Tropical peatland fires generate substantial quantities of airborne fine particulate matter (PM_2.5) and in Indonesia are intensified during El Niño-related drought leading to severe air quality impacts affecting local and distant populations. Limited in-situ data often necessitates reliance on air quality models, like that of the Copernicus Atmosphere Monitoring Service, whose accuracy in extreme conditions is not fully understood. Here we demonstrate how a network of low-cost sensors around Palangka Raya, Central Kalimantan during the 2019 fire season, quantified extreme air quality and city-scale variability. The data indicates relatively strong model performance. Health impacts are substantial with estimates of over 1200 excess deaths in the Palangka Raya region, over 3200 across Central Kalimantan and more than 87,000 nationwide in 2019 due to fire-induced PM_2.5 exposure. These findings highlight the need for urgent action to mitigate extreme fire events, including reducing fire use and landscape remediation to prevent peat fire ignition.

Emission inventory of pollutants (CO, SO₂, PM_2,5, and NO_x) from various sectors included power plants, road transportation, industry, and residential and commercial was conducted in Jakarta for the year 2005-2015. Emissions load of these pollutants was calculated using Gains (Greenhouse gas model - Air pollution Interactions and Synergies) model, based on energy used. Control technology was also applied for pollutants emitted from power plant, industry as well as residential and commercial, while control of EURO II and EURO III were considered for transportation sector. Emission inventory result shows that in 2015, the total emission load for NO_x, and CO were estimated around 52.9 kton and 143.9 kton respectively. The biggest contributor to these pollutants were from road transportation sector which contributed of 57%, and 93% for NO_x, and CO respectively. From transportation sector, heavy duty vehicles is the biggest contributor of NO_x and BC emission, while for CO is mostly emitted from motorcycles. While total emission load for PM_2.5 in Jakarta is about 4.6 kton, and it is mostly emitted from road transportation and industrial combustion sectors which are 2.1 kton (46%) and 0.4 kton (43%), respectively. The heavy duty vehicles was still the highest contributor of PM_2.5 emission in the transport sector. The total SO₂ emission load is 19.7 kton, and it is mostly released from industrial combustion that contribute almost 67% of total emission.

Extreme fires in the peatlands of South East (SE) Asia are arguably the world’s greatest biomass burning events, resulting in some of the worst ambient air pollution ever recorded (PM₁₀ > 3000 µg·m⁻³). The worst of these fires coincide with El Niño related droughts, and include huge areas of smouldering combustion that can persist for months. However, areas of flaming surface vegetation combustion atop peat are also seen, and we show that the largest of these latter fires appear to be the most radiant and intensely smoke-emitting areas of combustion present in such extreme fire episodes. Fire emissions inventories and early warning of the air quality impacts of landscape fire are increasingly based on the fire radiative power (FRP) approach to fire emissions estimation, including for these SE Asia peatland fires. “Top-down” methods estimate total particulate matter emissions directly from FRP observations using so-called “smoke emission coefficients” [C_e; g·MJ⁻¹], but currently no discrimination is made between fire types during such calculations. We show that for a subset of some of the most thermally radiant peatland fires seen during the 2015 El Niño, the most appropriate C_e is around a factor of three lower than currently assumed (~16.8 ± 1.6 g·MJ⁻¹ vs. 52.4 g·MJ⁻¹). Analysis indicates that this difference stems from these highly radiant fires containing areas of substantial flaming combustion, which changes the amount of particulate matter emitted per unit of observable fire radiative heat release in comparison to more smouldering dominated events. We also show that even a single one of these most radiant fires is responsible for almost 10% of the overall particulate matter released during the 2015 fire event, highlighting the importance of this fire type to overall emission totals. Discriminating these different fires types in ways demonstrated herein should thus ultimately improve the accuracy of SE Asian fire emissions estimates derived using the FRP approach, and the air quality modelling which they support.