Energy is vital to human society but significantly contributes to the deterioration of environmental quality and the global issue of climate change. Biomass and fossil fuels are important energy sources but have distinct pollutant emission characteristics during the burning process. This study aimed at attributing radiative forcing of climate forcers, including greenhouse gases but also short-lived climate pollutants, from the burning of fossil and biomass fuels, and the spatiotemporal characteristics. We found that air pollutant emissions from the burning process of biofuel and fossil fuels induced RFs of 68.2 ± 36.8 mW m⁻² and 840 ± 225 mW m⁻², respectively. The relatively contribution of biomass burning emissions was 7.6% of that from both fossil and biofuel combustion processes, while its contribution in energy supply was 11%. These relative contributions varied obviously across different regions. The per unit energy consumption of biomass fuel in the developed regions, such as North America (0.57 ± 0.33 mW m⁻²/10⁷TJ) and Western Europe (0.98 ± 0.79 mW m⁻²/10⁷TJ), had higher impacts of combustion emission related RFs compared to that of developing regions, like China (0.40 ± 0.26 mW m⁻²/10⁷TJ), and South and South-East Asia (0.31 ± 0.71 mW m⁻²/10⁷TJ) where low efficiency biomass burning in residential sector produced significant amounts of organic matter that had a cooling effect. Note that the study only evaluated fuel combustion emission related RFs, and those associated with the production of fuels and land use change should be studied later in promoting a comprehensive understanding on the climate impacts of biomass utilization.

Fine particles (PM2.5) samples, collected at Weizhou Island over the Gulf of Tonkin on a daytime and nighttime basis in the spring of 2015, were analyzed for primary and secondary organic tracers, together with organic carbon (OC), elemental carbon (EC), and stable carbon isotopic composition (δ13C) of total carbon (TC). Five organic compound classes, including saccharides, lignin/resin products, fatty acids, biogenic SOA tracers and phthalic acids, were quantified by gas chromatography/mass spectrometry (GC/MS). Levoglucosan was the most abundant organic species, indicating that the sampling site was under strong influence of biomass burning. Based on the tracer-based methods, the biomass-burning-derived fraction was estimated to be the dominant contributor to aerosol OC, accounting for 15.7% ± 11.1% and 22.2% ± 17.4% of OC in daytime and nighttime samples, respectively. In two episodes E1 and E2, organic aerosols characterized by elevated concentrations of levoglucosan as well as its isomers, sugar compounds, lignin products, high molecular weight (HMW) fatty acids and β-caryophyllinic acid, were attributed to the influence of intensive biomass burning in the northern Southeast Asia (SEA). However, the discrepancies in the ratios of levoglucosan to mannosan (L/M) and OC (L/OC) as well as the δ13C values suggest the type of biomass burning and the sources of organic aerosols in E1 and E2 were different. Hardwood and/or C4 plants were the major burning materials in E1, while burning of softwood and/or C3 plants played important role in E2. Furthermore, more complex sources and enhanced secondary contribution were found to play a part in organic aerosols in E2. This study highlights the significant influence of springtime biomass burning in the northern SEA to the organic molecular compositions of marine aerosols over the Gulf of Tonkin.

The interactions between aerosols, clouds, and precipitation remain among the largest sources of uncertainty in the Earth's energy budget. Biomass-burning aerosols are a key feature of the global aerosol system, with significant annually-repeating fires in several parts of the world, including Southeast Asia (SEA). SEA in particular provides a “natural laboratory” for these studies, as smoke travels from source regions downwind in which it is coupled to persistent stratocumulus decks. However, SEA has been under-exploited for these studies. This review summarizes previous related field campaigns in SEA, with a focus on the ongoing Seven South East Asian Studies (7-SEAS) and results from the most recent BASELInE deployment. Progress from remote sensing and modeling studies, along with the challenges faced for these studies, are also discussed. We suggest that improvements to our knowledge of these aerosol/cloud effects require the synergistic use of field measurements with remote sensing and modeling tools.

Microplastic ingestion has been documented in various aquatic species. This causes physical damage, and additionally contaminated microplastics transfer attached pollutants and microbial pathogens to ingesting organisms. Continued metal accumulation can lead to toxicity and adverse health effects; attached microbial pathogens can cause dysbiosis - which lowers host immunity and promotes infections. Catfish, Clarias gariepinus, are a major food source in Southeast Asia, a hotspot of plastic pollution. This study aimed to quantify the transfer of the trace metals copper (Cu) and lead (Pb) -at environmentally relevant concentrations-from microplastics (polyamide 12, PA12, and polylactic acid, PLA) to catfish. Fish were reared for three months and exposed to seven different combinations of feed, supplemented with plastics and metals. At monthly intervals, fish gills, intestines, liver, and edible muscles were analysed for Cu and Pb concentrations using ICP-OES, and the intestines content assessed for Vibrio sp.. Our results showed that biodegradable PLA transferred higher amounts of metals to catfish than expected and also led to increased Vibrio counts in the intestines compared to PA12. Trace metal accumulation was significantly different in varying tissues, with highest concentrations observed in the gills, followed by liver, intestines, and lastly edible muscles. The results of this study further support the existing evidence that microplastics act as efficient shuttles to concentrate and transfer metals. They also indicate that their uptake can cause dysbiosis (increased numbers of Vibrio sp.). Most importantly, however, our study highlights that biodegradable polymers, such as PLA, could actually pose a greater environmental threat when ingested compared to the more common polymers such as PA12.

In this study, various remote sensing data, modeling data and emission inventories were integrated to analyze the tempo-spatial distribution of biomass burning in mainland Southeast Asia and its effects on the local ambient air quality from 2001 to 2016. Land cover changes have been considered in dividing the biomass burning into four types: forest fires, shrubland fires, crop residue burning and other fires. The results show that the monthly average number of fire spots peaked at 34,512 in March and that the monthly variation followed a seasonal pattern, which was closely related to precipitation and farming activities. The four types of biomass burning fires presented different tempo-spatial distributions. Moreover, the monthly Aerosol Optical Depth (AOD), concentration of particulate matter with a diameter less than 2.5 μm (PM₂.₅) and carbon monoxide (CO) total column also peaked in March with values of 0.62, 45 μg/m³ and 3.25 × 10¹⁸ molecules/cm², respectively. There are significant correlations between the monthly means of AOD (r = 0.74, P < 0.001), PM₂.₅ concentration (r = 0.88, P < 0.001), and CO total column (r = 0.82, P < 0.001) and the number of fire spots in the fire season. We used Positive Matrix Factorization (PMF) model to resolve the sources of PM₂.₅ into 3 factors. The result indicated that the largest contribution (48%) to annual average concentration of PM₂.₅ was from Factor 1 (dominated by biomass burning), followed by 27% from Factor 3 (dominated by anthropogenic emission), and 25% from Factor 2 (long-range transport/local nature source). The annually anthropogenic emission of CO and PM₂.₅ from 2001 to 2012 and the monthly emission from the Emission Database for Global Atmosphere Research (EDGAR) were consistent with PMF analysis and further prove that biomass burning is the dominant cause of the variation in the local air quality in mainland Southeast Asia.

Atmospheric deposition, either dry or wet, has been identified as an important pathway of mercury (Hg) input to terrestrial and aquatic systems. Although East Asia is the major atmospheric Hg emission source region, very few studies have been conducted to quantify atmospheric Hg deposition in its downwind region. In this study, 8-year (2009–2016) atmospheric Hg dry deposition was reported at the Lulin Atmospheric Background Station (LABS), a high mountain forest site in central Taiwan. Dry deposition of speciated Hg was estimated using a bi-directional air-surface flux exchange model for gaseous elemental mercury (GEM) and dry deposition models for gaseous oxidized mercury (GOM) and particulate-bound mercury (PBM), making use of the monitored speciated atmospheric Hg concentrations. Annual total Hg dry deposition ranged from 51.9 to 84.9 μg m−2 yr−1 with a multi-year average of 66.1 μg m−2 yr−1. Among the three forms of atmospheric Hg, GEM was the main contributor to the total dry deposition, contributing about 77.8% to the total, due to the high density of forest canopy as well as the much higher concentration of GEM than GOM and PBM at LABS. Mercury dry deposition is higher in winter and spring than in summer and fall, partly due to the elevated Hg concentrations associated with air masses from East and Southeast Asia where with high atmospheric Hg emissions. The mean annual dry/wet deposition ratio of 2.8 at LABS indicated that Hg deposition to forest landscape was governed by dry rather than wet deposition.

The present study utilizes 18 years of long-term (2001–2018) data collected from six active AERONET sites over the Indo-Gangetic Plain (IGP) and the North China Plain (NCP) areas in Southeast Asia. The annual mean (±SD) aerosol optical thickness at 440 nm (AOT₄₄₀) was found high at XiangHe (0.92 ± 0.69) and Taihu (0.90 ± 0.51) followed by Beijing (0.81 ± 0.69), Lahore (0.81 ± 0.43), and Kanpur (0.73 ± 0.35) and low at Karachi (0.52 ± 0.23). Seasonally, high AOT₄₄₀ with corresponding high Ångström exponent (ANG₄₄₀₋₈₇₀) noticed during JJA for all sites, except Kanpur, suggesting the dominance of fine-mode particles, generally associated with large anthropogenic emissions. Climatologically, an increasing (decreasing) trend was observed over IGP (NCP) sites, with the highest (lowest) percentage of departures in AOT₄₄₀ found over Beijing (Karachi). We further identified major aerosol types which showed the dominance of biomass burning, urban-industrial followed by the mixed type of aerosols. In addition, single scattering albedo (SSA), asymmetry parameter (ASP), volume size distribution (VSD), and complex aerosol refractive index (RI) showed significant temporal and spectral changes, illustrating the complexity of aerosol types. At last, the annual mean direct aerosol radiative forcing at the top, bottom, and within the atmosphere for all sites were found in the range from −17.36 ± 3.75 to −45.17 ± 4.87 W m⁻², -64.6 ± 4.86 to −93.7 ± 10.27 W m⁻², and 40.5 ± 6.43 to 68.25 ± 7.26 W m⁻², respectively, with an averaged atmospheric heating rate of 0.9–2.3 K day⁻¹. A large amount of anthropogenic aerosols showed a significant effect of heating (cooling) on the atmosphere (surface) results obviously, due to an increased rate of atmospheric heating. Therefore, the thermodynamic effects of anthropogenic aerosols on the atmospheric circulation and its structure should be taken into consideration for future study over the experimental sites.

Fine particulate matter (PM₂.₅) concentrations exhibit distinct spatiotemporal heterogeneity, mainly due to the natural environment and human activities. Yunnan Province of China was selected as the research area, and a real-time measured PM₂.₅ concentration dataset was acquired from 41 monitoring stations in 16 major cities from February 2013 to December 2018. Aerosol optical depth (AOD) products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and data on four meteorological variables from 2000 to 2018 were employed. A novel hybrid model was constructed to estimate the historical missing PM₂.₅ values from 2000 to 2012, calculate the missing PM₂.₅ concentrations from 2012 to 2014 in some major cities, and analyze the driving factors of the PM₂.₅ concentration changes and causes of key pollution events in Yunnan Province over the past 19 years. The temporal analysis results indicate that the annual mean PM₂.₅ concentration in Yunnan Province exhibited three stages: continuous stability, a rapid increase and a rapid decrease. The year 2013 was an important breakpoint in the trend of the concentration change. The spatial analysis results reveal that the annual mean PM₂.₅ concentration in the north was lower than that in the south, and there was a significant difference between the east and the west. In addition, springtime biomass burning in Southeast Asia was found to be the main cause of PM₂.₅ pollution in Yunnan Province in spring.

A Community Multiscale Air Quality (CMAQ) model with source-oriented lumped SAPRC-11 (S11L) photochemical mechanism and secondary organic aerosol (SOA) module was applied to determine the contributions of anthropogenic and biogenic sources to SOA concentrations in China. A one-year simulation of 2013 using the Multi-resolution Emission Inventory for China (MEIC) shows that summer SOA are generally higher (10–15 μg m−3) due to large contributions of biogenic (country average 60%) and industrial sources (17%). In winter, SOA formation was mostly due to anthropogenic emissions from industries (40%) and residential sources (38%). Emissions from other countries in southeast China account for approximately 14% of the SOA in both summer and winter, and 46% in spring due to elevated open biomass burning in southeast Asia. The Regional Emission inventory in ASia v2.1 (REAS2) was applied in this study for January and August 2013. Two sets of simulations with the REAS2 inventory were conducted using two different methods to speciate total non-methane carbon into model species. One approach uses total non-methane hydrocarbon (NMHC) emissions and representative speciation profiles from the SPECIATE database. The other approach retains the REAS2 speciated species that can be directly mapped to S11L model species and uses source specific splitting factors to map other REAS2 lumped NMHC species. Biogenic emissions are still the most significant contributor in summer based on these two sets of simulations. However, contributions from the transportation sector to SOA in January are predicted to be much more important based on the two REAS2 emission inventories (∼30–40% vs. ∼5% by MEIC), and contributions from residential sources according to REAS2 was much lower (∼21–24% vs. ∼42%). These discrepancies in source contributions to SOA need to be further investigated as the country seeks for optimal emission control strategies to fight severe air pollution.