Molecular markers for biomass burning associated with the characterization of PM2.5 and component sources during dry season haze episodes in Upper South East Asia

Is replacing missing values of PM2.5 constituents with estimates using machine learning better for source apportionment than exclusion or median replacement?

East Asian countries have been conducting source apportionment of fine particulate matter (PM2.5) by applying positive matrix factorization (PMF) to hourly constituent concentrations. However, some of the constituent data from the supersites in South Korea was missing due to instrument maintenance and calibration. Conventional preprocessing of missing values, such as exclusion or median replacement, causes biases in the estimated source contributions by changing the PMF input. Machine learning (ML) can estimate the missing values by training on constituent data, meteorological data, and gaseous pollutants. Complete data from the Seoul Supersite in 2018 was taken, and a random 20% was set as missing. PMF was performed by replacing missing values with estimates. Percent errors of the source contributions were calculated compared to those estimated from complete data. Missing values were estimated using a random forest analysis. Estimation accuracy (r2) was as high as 0.874 for missing carbon species and low at 0.631 when ionic species and trace elements were missing. For the seven highest contributing sources, replacing the missing values of carbon species with estimates minimized the percent errors to 2.0% on average. However, replacing the missing values of the other chemical species with estimates increased the percent errors to more than 9.7% on average. Percent errors were maximal at 37% on average when missing values of ionic species and trace elements were replaced with estimates. Missing values, except for carbon species, need to be excluded. This approach reduced the percent errors to 7.4% on average, which was lower than those due to median replacement. Our results show that reducing the biases in source apportionment is possible by replacing the missing values of carbon species with estimates. To improve the biases due to missing values of the other chemical species, the estimation accuracy of the ML needs to be improved.

Characterizing Chemical, Environmental, and Stimulated Subcellular Physical Characteristics of Size-Fractionated PMs Down to PM0.1

Air pollution, especially particulate matter (PM), is a significant environmental pollution worldwide. Studying the chemical, environmental, and life-related cellular physical characteristics of size-fractionated PMs is important because of their different degrees of harmful effects on human respiratory tracts and organ systems, causing severe diseases. This study evaluates the chemical components of size-fractionated PMs down to PM0.1 collected during a biomass-burning episode, including elemental/organic carbon and trace elements. Single particle sizes and distributions of PM0.1, PM0.5-0.1, PM1.0-0.5, and PM2.5-1.0 were analyzed by scanning electron microscopy and Zeta sizer. Two commonly used cell lines, e.g., HeLa and Cos7 cells, and two respiratory-related cell lines including lung cancer/normal cells were utilized for cell cytotoxicity experiments, revealing the key effects of particle sizes and concentrations. A high-speed scanning ion conductance microscope explored particle-stimulated subcellular physical characteristics for all cell lines in dynamics, including surface roughness (SR) and elastic modulus (E). The statistical results of SR showed distinct features among different particle sizes and cell types while a E reduction was universally found. This work provides a comprehensive understanding of the chemical, environmental, and cellular physical characteristics of size-fractionated PMs and sheds light on the necessity of controlling small-sized PM exposures.

Reframing the wicked problem of pre-harvest burning: A case study of Thailand's sugarcane

Pre-harvest sugarcane burning persists in many countries though there are policies prohibiting the practice. As problems related to sugarcane harvesting are complex, a thorough understanding of the problems for policy formulation is required. The objective of this study was to reanalyze or reframe problems of sugarcane harvesting and pre-harvest sugarcane burning. Concepts of wicked problems, practical reasoning and policy reframing were applied. The study used a participatory modeling approach to illustrate the case of Thailand. Wickedness was shown by complexity and uncertainties of factors intertwining with values related to adoption of harvesting methods; green mechanical, green manual and burnt manual. As timeliness of harvest was the top priority, the burnt method was considered more efficient. It was easier, faster, cheaper and more suitable under unfavorable circumstances for the green methods. The policy to reduce burnt-harvested sugarcane was not so effective and also led to the undesired 'green but unclean' method. To frame harvesting problems based on emissions of fine particulates (PM2.5) from sugarcane burning was not a good choice. Incomplete problem sense-making and poor problem frame were indicated. Most farmers were unable to associate sugarcane burning with environmental problems of PM2.5 (and also global warming/climate change) and livelihood impacts. Nevertheless, a larger concern over climate variations was perceived by a majority of farmers. Farmers who adapted relied primarily on green harvesting and the use of residues as trash blankets. Through policy reframing, inefficient green harvesting was seen as a better frame. The new frame enabled farmers linking agricultural practices to sustainability of environment, productivity and livelihoods in the context of climate change. Using participatory modeling for reframing policy problems in general and wicked problems in particular was shown to be powerful and contributing to originality.

Influence of meteorological factors on open biomass burning at a background site in Northeast China

Biomass burning (BB) is a very important emission source that significantly adversely impacts regional air quality. BB produces a large number of primary organic aerosol (POA) and black carbon (BC). Besides, BB also provides many precursors for secondary organic aerosol (SOA) generation. In this work, the ratio of levoglucosan (LG) to organic carbon (OC) and the fire hotspots map was used to identify the open biomass burning (OBB) events, which occurred in two representative episodes, October 13 to November 30, 2020, and April 1 to April 30, 2021. The ratio of organic aerosol (OA) to reconstructed PM2.5 concentration (PM2.5*) increased with the increase of LG/OC. When LG/OC ratio is higher than 0.03, the highest OA/PM2.5* ratio can reach 80%, which means the contribution of OBB to OA is crucial. According to the ratio of LG to K+, LG to mannosan (MN) and the regional characteristics of Longfengshan, it can be determined that the crop residuals are the main fuel. The occurrence of OBB coincides with farmers' preferred choices, i.e., burning biomass in "bright weather". The "bright weather" refers to the meteorological conditions with high temperature, low humidity, and without rain. Meteorological factors indirectly affect regional biomass combustion pollution by influencing farmers' active choices.

Combined use of principal component analysis/multiple linear regression analysis and artificial neural network to assess the impact of meteorological parameters on fluctuation of selected PM2.5-bound elements

Based on the data of the State of Global Air (2020), air quality deterioration in Thailand has caused ~32,000 premature deaths, while the World Health Organization evaluated that air pollutants can decrease the life expectancy in the country by two years. PM2.5 was collected at three air quality observatory sites in Chiang-Mai, Bangkok, and Phuket, Thailand, from July 2020 to June 2021. The concentrations of 25 elements (Na, Mg, Al, Si, S, Cl, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Se, Br, Sr, Ba, and Pb) were quantitatively characterised using energy-dispersive X-ray fluorescence spectrometry. Potential adverse health impacts of some element exposures from inhaling PM2.5 were estimated by employing the hazard quotient and excess lifetime cancer risk. Higher cancer risks were detected in PM2.5 samples collected at the sampling site in Bangkok, indicating that vehicle exhaust adversely impacts human health. Principal component analysis suggests that traffic emissions, crustal inputs coupled with maritime aerosols, and construction dust were the three main potential sources of PM2.5. Artificial neural networks underlined agricultural waste burning and relative humidity as two major factors controlling the air quality of Thailand.

Molecular characterization of organic aerosols over the Tibetan Plateau: Spatiotemporal variations, sources, and potential implications

Organic aerosols have profound and far-reaching influences on the Earth's climate, ecosystems, environmental quality, and public health. Elucidating the precise composition and sources of these aerosols over the Tibetan Plateau, a region highly sensitive to climate change and vulnerable to ecosystems, is critically important. Sixteen organic molecular tracers in aerosols were quantified using solvent extraction-BSTFA derivatization, and GC/MS analysis at six sites over the Tibetan Plateau during 2014 and 2016. Average total tracer concentration was 32.5 ± 20.1 ng m-3. The highest levels of biomass burning tracers (anhydrosugars and aromatic acids) were found at southeastern Tibetan Plateau site Yulong (20.8 ± 21.3 ng m-3) followed by the western site Ngari (13.3 ± 10.6 ng m-3). Biomass burning tracers decreased from southern sites like Everest (9.50 ± 10.5 ng m-3) to northern aeras such as Laohugou (2.59 ± 2.19 ng m-3). Biomass burning tracers peaked in non-monsoon seasons while primary saccharides and sugar alcohols predominated during monsoon months. Using tracer-based methods, biomass burning contributed 0.4%-8.4% of organic carbon over the plateau, with higher non-monsoon contributions (3.6% ± 3.7%). Backward air mass trajectories and fire spots indicated South Asian biomass burning impacts on organic aerosols at western, southern, and southeastern Tibetan Plateau sites, particularly in non-monsoon periods. Fungal spores and plant debris comprised 0.6%-6.3% and 0.3%-1.2% of organic carbon respectively, with higher monsoon contributions (4.2% ± 4.7%) of fungal spores. Secondary organic carbon was estimated to contribute substantially (45.5%-73.5%) over the plateau but requires further investigation. These results provide insights into pollution mitigation and the assessments of climate and ecology changes for the Tibetan Plateau.

Contrasting compositions of PM2.5 in Northern Thailand during La Niña (2017) and El Niño (2019) years

There have been a very limited number of systematic studies on PM2.5 compositions and their source contribution in Southeast Asia. This study aims to explore the characteristics of PM2.5 composition collected in Chiang Mai (Thailand) during La Niña and El Niño years and to apportion their sources during smoke haze and non-haze periods. The average PM2.5 concentration of smoke haze episode in 2019 (El Niño) was much higher than in 2017 (La Niña). The ratios of organic carbon (OC) to elemental carbon (EC), as well as K (biomass burning (BB) tracer) to PM2.5, were higher during smoke haze episodes in 2019 than in 2017 indicating a significant influence from BB. The ratios of secondary organic carbon (SOC) levels to primary organic carbon (POC) levels during smoke haze episodes were higher than those in non-haze period, which indicated greater SOC contributions or more photo-oxidation of precursors in haze episodes with high ambient temperatures. However, the ratios of soil markers (Ca and Mg) during non-haze period were high implying that soil source contributed more to PM2.5 concentrations when there less BB occurred. The positive Matrix Factorization (PMF) model revealed that the source of BB, characterized by high K fractions, was the largest contributor during smoke haze episodes accounting for 50% (2017) and 79% (2019). Climate conditions influence meteorological patterns, particularly during incidences of extreme weather such as droughts, which affect the scale and frequency of open burning and thus air pollution levels.

Chemical composition, sources, and health risks of PM2.5 in small cities with different urbanization during 2020 Chinese Spring Festival

To investigate the impact of quarantine measures and fireworks banning policy on chemical composition and sources of PM2.5 and associated health risks in small, less developed cities, we sampled in Guigang (GG), Shaoyang (SY), and Tianshui (TS), located in eastern, central, and north-western China, in 2020 Spring Festival (CSF). Mass concentration, carbonaceous, metals, and WSIIs of PM2.5 were analyzed. The study found high levels of PM2.5 pollution with the average concentration of 168.05 µg/m3 in TS, 134.59 µg/m3 in SY, and 125.71 µg/m3 in GG. A negative correlation was found between the urbanization level and PM2.5 pollution. Lockdown measures reduced PM2.5 mass and industrial elements. In non-control period (NCP), combustion and fireworks were the major sources of PM2.5 in GG and TS, and industry source accounted for a significant proportion in the relatively more urbanized SY. Whereas on control period (CP), soil dust, combustion, and road dust were the main source in GG, secondary aerosols dominated in SY and TS. Our health risk assessment showed unacceptable levels of non-carcinogenic and carcinogenic risks over the study areas, despite lockdown measures reducing health risks. As and Cr(VI), as the major pollutants, their associated sources, industry sources, and fireworks sources, posed the greatest risk to people at the sampling sites after exposure to PM2.5. This work supports the improvement of PM2.5 control strategies in small Chinese cities during the CSF.

Characterizing atmospheric biological aerosols at a suburban site in Guangzhou, southern China by airborne microbes, proteins and saccharides

Bioaerosols in ambient environment can be evaluated using various techniques. However, the results of bioaerosols obtained using different methods are rarely compared. The relationships between different bioaerosol indicators and their behaviors under the influence of environment factors are seldom investigated. Here we used airborne microbial numbers, proteins and saccharides concentrations as the indicators to characterize bioaerosols in two seasons with different source contribution, air pollution situation and meteorological conditions. The observation was conducted at a suburban site in Guangzhou, southern China, during the winter and spring periods of 2021. Airborne microbes were observed with an average of (1.82 ± 1.33) × 10⁶ cells/m³, converted to the mass concentration level of 0.42 ± 0.30 μg/m³, comparable but lower than that of proteins (0.81 ± 0.48 μg/m³). Both of them were much higher than the average concentration of saccharides (19.93 ± 11.53 ng/m³). During the winter period, significant and good correlations were observed between the three components. In spring, a biological outbreak was observed in late March with a strong elevation of airborne microbes followed by elevations of proteins and saccharides. The retardation of proteins and saccharides could be the result of the enhanced release from microorganisms under the influence of atmospheric oxidation processes. Saccharides in PM_2.5 were studied to reveal the contribution of specific sources of bioaerosols (e.g. fungi, pollen, plants and soil). Our results show that primary emissions and secondary processes should play their roles in the variations of these biological components. By comparing the results of the three methods, this study provides an insight into the applicability and variability of bioaerosols characterization in the ambient environment with respect to various influences of sources, atmospheric processes and environmental conditions.

Airborne particulate matter from biomass burning in Thailand: Recent issues, challenges, and options

Many of the current atmospheric environmental problems facing Thailand are linked to air pollution that is largely derived from biomass burning. Different parts of Thailand have distinctive sources of biomass emissions that affect air quality. The main contributors to atmospheric particulate matter (PM), especially the PM_2.5 fraction in Thailand, were highlighted in a recent study of PM derived from biomass burning. This review is divided into six sections. Section one is an introduction to biomass burning in Thailand. Section two covers issues related to biomass burning for each of the four main regions in Thailand, including Northern, Northeastern, Central, and Southern Thailand. In northern Thailand, forest fires and the burning of crop residues have contributed to air quality in the past decade. The northeast region is mainly affected by the burning of agricultural residues. However, the main contributor to PM in the Bangkok Metropolitan Region is motor vehicles and crop burning. In Southern Thailand, the impact of agoindustries, biomass combustion, and possible agricultural residue burning are the primary sources, and cross-border pollution is also important. The third section concerns the effect of biomass burning on human health. Finally, perspectives, new challenges, and policy recommendations are made concerning improving air quality in Thailand, e.g., forest fuel management and biomass utilization. The overall conclusions point to issues that will have a long-term impact on achieving a blue sky over Thailand through the development of coherent policies and the management of air pollution and sharing this knowledge with a broader audience.

Regional monitoring of biomass burning using passive air sampling technique reveals the importance of MODIS unresolved fires

Field-based sampling can provide more accurate evaluation than MODIS in regional biomass burning (BB) emissions given the limitations of MODIS on unresolved fires. Polyurethane foam-based passive air samplers (PUF-PASs) are a promising tool for collecting atmospheric monosaccharides. Here, we deployed PUF-PASs to monitor monosaccharides and other BB-related biomarkers and presented a dataset of 31 atmospheric BB-related biomarkers in the Indo-China Peninsula (ICP) and Southwest China. The peak concentrations of monosaccharides in the ICP occurred before monsoon season. The highest concentrations were in the eastern Mekong plain, while the lowest were along the eastern coast. BB-related biomarkers displayed elevated concentrations after April, particularly in the monsoon season; however, fewer active fires were recorded by MODIS. This revealed the importance of MODIS unresolved fires (e.g., indoor biofuel combustion, small-scale BB incidents, and charcoal fires) to the regional atmosphere. The PAS derived levoglucosan concentrations indicated that, with the inclusion of MODIS unresolved fires, the estimated top-down emissions of PM (4194-4974 Gg/yr), OC (1234-1719 Gg/yr) and EC (52-384 Gg/yr) would be higher than previous bottom-up estimations in the ICP. Future studies on these MODIS unresolved fires and regional monitoring data of BB are vital for improving the modeling of regional BB emissions.

GIS-based air quality modelling: spatial prediction of PM10 for Selangor State, Malaysia using machine learning algorithms

Rapid urbanization has caused severe deterioration of air quality globally, leading to increased hospitalization and premature deaths. Therefore, accurate prediction of air quality is crucial for mitigation planning to support urban sustainability and resilience. Although some studies have predicted air pollutants such as particulate matter (PM) using machine learning algorithms (MLAs), there is a paucity of studies on spatial hazard assessment with respect to the air quality index (AQI). Incorporating PM in AQI studies is crucial because of its easily inhalable micro-size which has adverse impacts on ecology, environment, and human health. Accurate and timely prediction of the air quality index can ensure adequate intervention to aid air quality management. Therefore, this study undertakes a spatial hazard assessment of the air quality index using particulate matter with a diameter of 10 μm or lesser (PM10) in Selangor, Malaysia, by developing four machine learning models: eXtreme Gradient Boosting (XGBoost), random forest (RF), K-nearest neighbour (KNN), and Naive Bayes (NB). Spatially processed data such as NDVI, SAVI, BU, LST, Ws, slope, elevation, and road density was used for the modelling. The model was trained with 70% of the dataset, while 30% was used for cross-validation. Results showed that XGBoost has the highest overall accuracy and precision of 0.989 and 0.995, followed by random forest (0.989, 0.993), K-nearest neighbour (0.987, 0.984), and Naive Bayes (0.917, 0.922), respectively. The spatial air quality maps were generated by integrating the geographical information system (GIS) with the four MLAs, which correlated with Malaysia's air pollution index. The maps indicate that air quality in Selangor is satisfactory and posed no threats to health. Nevertheless, the two algorithms with the best performance (XGBoost and RF) indicate that a high percentage of the air quality is moderate. The study concludes that successful air pollution management policies such as green infrastructure practice, improvement of energy efficiency, and restrictions on heavy-duty vehicles can be adopted in Selangor and other Southeast Asian cities to prevent deterioration of air quality in the future.

Saccharides in atmospheric PM2.5 in tropical forest region of southwest China: Insights into impacts of biomass burning on organic carbon aerosols

Biomass burning (BB) in South and Southeast Asia has a strong impact on regional air quality, yet its impact on atmospheric PM_2.5 of tropical rainforest regions, a background region occupying a large area in South Asia, has rarely been investigated. In this work, we performed one-year PM_2.5 sampling during December 2018 to October 2019 at a tropical rainforest site in southwest China. PM_2.5 mass concentration, major chemical components, and ten saccharides were determined to study seasonal variations of PM_2.5 chemical composition, and further to understand possible impacts of BB to organic carbon (OC) aerosols at this background region. The concentration levels of PM_2.5, major PM_2.5 components, and total saccharides were significantly higher in dry season than in wet season. Besides, PM_2.5, OC, and total saccharides were highly correlated (R² > 0.64, p < 0.001) during the sampling period, suggesting they might share common sources. Source apportionment of saccharides revealed that BB was the main source in both seasons. Furthermore, the contributions of BB to OC (BB/OC) were estimated using levoglucosan as a molecular tracer while levoglucosan's chemical degradation was considered. It was found that over 80% of LG was degraded in both seasons, suggesting BB sources were largely from the transport of external air mass. The estimated BB/OC were over 50%, indicating BB was an important source of OC and likely of PM_2.5 in both seasons. The air-mass backward trajectory analysis and active fire spots data indicate intense BB emission sources were from South and Southeast Asia in dry season and the BB emissions in southern region of China could impact on the studied area in wet season.

Tracing the biomass burning emissions over the Arabian Sea in winter season: Implications from the molecular distributions and relative abundances of sugar compounds

The widespread haze pollution over South Asia typically occurs in winter, affecting the abundance of organic aerosols (OA) over the Arabian Sea due to prevailing meteorology. We determined the concentrations of biomass burning (BB) derived anhydrosugars (levoglucosan: Lev, galactosan: Gal, and mannosan: Man), sugars (glucose, fructose, sucrose, and trehalose) and sugar alcohols (arabitol, mannitol, erythritol, and inositol) over the Arabian Sea during a winter cruise (6-24 December 2018). Molecular distributions revealed predominance of levoglucosan or sucrose. Besides, levoglucosan strongly correlated with mannosan, galactosan, sugar alcohols and elemental carbon, emphasizing their BB-origin. Backward air mass trajectories intercepted by the satellite-based fire counts over the Indo-Gangetic Plain together with relationship between stable carbon isotopic composition of TC (δ¹³C_TC) and levoglucosan-C to organic carbon (%), confirmed the impact of BB-derived OA. A comparison of Lev/Man (av. 16.2) and Lev/K⁺ (av. 0.27) ratios over the Arabian Sea with the source-emissions revealed their origin from crop-residue burning. Rather high concentrations of Lev over the Arabian Sea compared to those documented over the Bay of Bengal, East China Sea, Sea of Japan and the western North Pacific further suggests a stronger impact of BB in the continental outflow over this marine basin. Besides, Lev/K⁺ ratios in marine aerosols exponentially decreased with an apparent increase in ambient relative humidity and temperature over the Arabian Sea during the South Asian outflow, implying a photochemical oxidation of BBOA. Such field-based relationship of Lev with the meteorological parameters can be useful for modelling the impact of BBOA on the surface Ocean. Besides, the aeolian input of sugar-C and water-soluble organic carbon (WSOC) accounted for 83 % and 92 % of that riverine supply to the Arabian Sea, respectively. This means atmospheric dry-deposition of sugars is an important external source of dissolved organic compounds to the surface water.

Wildfire Smoke Influence on Cloud Water Chemical Composition at Whiteface Mountain, New York

Wildfires significantly impact air quality and climate, including through the production of aerosols that can nucleate cloud droplets and participate in aqueous-phase reactions. Cloud water was collected during the summer months (June-September) of 2010-2017 at Whiteface Mountain, New York and examined for biomass burning influence. Cloud water samples were classified by their smoke influence based on backward air mass trajectories and satellite-detected smoke. A total of 1,338 cloud water samples collected over 485 days were classified by their probability of smoke influence, with 49% of these days categorized as having moderate to high probability of smoke influence. Carbon monoxide and ozone levels were enhanced during smoke influenced days at the summit of Whiteface Mountain. Smoke-influenced cloud water samples were characterized by enhanced concentrations of potassium, sulfate, ammonium, and total organic carbon, compared to samples lacking identified influence. Five cloud water samples were examined further for the presence of dissolved organic compounds, insoluble particles, and light-absorbing components. The five selected cloud water samples contained the biomass burning tracer levoglucosan at 0.02-0.09 μM. Samples influenced by air masses that remained aloft, above the boundary layer during transport, had lower insoluble particle concentrations, larger insoluble particle diameters, and larger oxalate:sulfate ratios, suggesting cloud processing had occurred. These findings highlight the influence that local and long-range transported smoke have on cloud water composition.

Source Identification of PM2.5 during a Smoke Haze Period in Chiang Mai, Thailand, Using Stable Carbon and Nitrogen Isotopes

Open biomass burning (BB) has contributed severely to the ambient levels of particulate matter of less than 2.5 μm diameter (PM2.5) in upper northern Thailand over the last decade. Some methods have been reported to identify the sources of burning using chemical compositions, i.e., ions, metals, polycyclic aromatic hydrocarbons, etc. However, recent advances in nuclear techniques have been limited in use due to their specific instrumentation. The aims of this study were to investigate the sources of ambient PM2.5 in Chiang Mai city using stable carbon (δ13C) and nitrogen isotopes (δ15N). The mean concentrations of total carbon (TC) and total nitrogen (TN) in PM2.5 were 12.2 ± 5.42 and 1.91 ± 1.07 μg/m3, respectively, whereas δ13C and δ15N PM2.5 were −26.1 ± 0.77‰ and 10.3 ± 2.86‰, respectively. This isotopic analysis confirmed that biomass burning was the source of PM2.5 and that C3 and C4 plants contributed about 74% and 26%, respectively. These study results confirm that the stable isotope is an important tool in identifying the sources of aerosols.

An Application of Artificial Neural Network to Evaluate the Influence of Weather Conditions on the Variation of PM2.5-Bound Carbonaceous Compositions and Water-Soluble Ionic Species

Previous studies have determined biomass burning as a major source of air pollutants in the ambient air in Thailand. To analyse the impacts of meteorological parameters on the variation of carbonaceous aerosols and water-soluble ionic species (WSIS), numerous statistical models, including a source apportionment analysis with the assistance of principal component analysis (PCA), hierarchical cluster analysis (HCA), and artificial neural networks (ANNs), were employed in this study. A total of 191 sets of PM2.5 samples were collected from the three monitoring stations in Chiang-Mai, Bangkok, and Phuket from July 2020 to June 2021. Hotspot numbers and other meteorological parameters were obtained using NOAA-20 weather satellites coupled with the Global Land Data Assimilation System. Although PCA revealed that crop residue burning and wildfires are the two main sources of PM2.5, ANNs highlighted the importance of wet deposition as the main depletion mechanism of particulate WSIS and carbonaceous aerosols. Additionally, Mg2+ and Ca2+ were deeply connected with albedo, plausibly owing to their strong hygroscopicity as the CCNs responsible for cloud formation.

The Impact of Long-Range Transport of Biomass Burning Emissions in Southeast Asia on Southern China

The long-range transport of biomass burning pollutants from Southeast Asia has a significant impact on air quality in China. In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) fire data and aerosol optical depth (AOD) products and the Tropospheric Monitoring Instrument (TROPOMI) carbon monoxide (CO) data were used to analyze the impact of air pollution caused by biomass burning in Southeast Asia on southern China. Results showed that Yunnan, Guangdong and Guangxi were deeply affected by biomass burning emissions from March to April during 2016–2020. Comparing the data for fires on the Indochinese Peninsula and southern provinces of China, it is obvious that the contribution of pollutants emitted by local biomass burning in China to air pollution is only a small possibility. The distribution of CO showed that the overall emissions increased greatly from March to April, and there was an obvious transmission process. In addition, the MODIS AOD in areas close to the national boundary of China is at a high level (>0.6), and the AOD in the southwest of Guangxi province and the southeast of Yunnan Province is above 0.8. Combined with a typical air pollution event in southern China, the UVAI combined with wind direction and other meteorological data showed that the pollutants were transferred from the Indochinese Peninsula to southern China under the southwest monsoon. The PM2.5 data from ground-based measurements and backward tracking were used to verify the pollutant source of the pollution event, and it was concluded that the degree of pollution in Yunnan, Guangxi and Guangdong provinces was related to the distance from the Indochinese Peninsula. Results indicate that it is necessary to carry out in-depth research on the impact of cross-border air pollution transport on domestic air quality as soon as possible and to actively cooperate with foreign countries to carry out pollution source research and control.

Saccharides as Particulate Matter Tracers of Biomass Burning: A Review

The adverse effects of atmospheric particulate matter (PM) on health and ecosystems, as well as on meteorology and climate change, are well known to the scientific community. It is therefore undeniable that a good understanding of the sources of PM is crucial for effective control of emissions and to protect public health. One of the major contributions to atmospheric PM is biomass burning, a practice used both in agriculture and home heating, which can be traced and identified by analyzing sugars emitted from the combustion of cellulose and hemicellulose that make up biomass. In this review comparing almost 200 selected articles, we highlight the most recent studies that broaden such category of tracers, covering research publications on residential wood combustions, open-fire or combustion chamber burnings and ambient PM in different regions of Asia, America and Europe. The purpose of the present work is to collect data in the literature that indicate a direct correspondence between biomass burning and saccharides emitted into the atmosphere with regard to distinguishing common sugars attributed to biomass burning from those that have co-causes of issue. In this paper, we provide a list of 24 compounds, including those most commonly recognized as biomass burning tracers (i.e., levoglucosan, mannosan and galactosan), from which it emerges that monosaccharide anhydrides, sugar alcohols and primary sugars have been widely reported as organic tracers for biomass combustion, although it has also been shown that emissions of these compounds depend not only on combustion characteristics and equipment but also on fuel type, combustion quality and weather conditions. Although it appears that it is currently not possible to define a single compound as a universal indicator of biomass combustion, this review provides a valuable tool for the collection of information in the literature and identifies analytes that can lead to the determination of patterns for the distribution between PM generated by biomass combustion.

Saccharides in atmospheric particulate and sedimentary organic matter: Status overview and future perspectives

Saccharides are omnipresent compounds in terrestrial and marine ecosystems. Since the 2000s, their role in environmental and geochemical studies has significantly increased, but only anhydrosaccharides (mainly levoglucosan) have been reviewed. Here we present the wider knowledge about saccharides in organic matter of aerosols, bottom sediments, soils, dust, and sedimentary rocks. The main purpose here is to characterize the possible sources of saccharides, as well as sacharol formation, seasonal variability, and the possible applications in environmental and paleoenvironmental interpretations. Different saccharide sources were designated, including biomass burning, and particulate matter such as pollen, spores, lichen, and fungi, as well as polysaccharide decomposition as possible inputs of monosaccharides. The main focus was on the most common saccharides encountered in environmental samples and sedimentary rocks. These are the mono- and disaccharides glucose, fructose, sucrose, and trehalose, and sacharols arabitol and mannitol. The anhydrosaccharides levoglucosan, mannosan, and galactosan were evaluated as ancient wildfire indicators and industrialization tracers found in lacustrine sediments starting from Pleistocene to contemporary deposits. However, other anhydrosaccharides like xylosan and arabinosan were also found as products of fossil wood burning. These anhydrosaccharides have the potential to be further tracers of hemicellulose burning. Additional recommendations are proposed for future research, including environmental and paleoenvironmental topics that need to be addressed.

Size-fractionated PM-bound PAHs in urban and rural atmospheres of northern Thailand for respiratory health risk assessment

Size-fractionated particulate matters (SPMs) in a range of 9.0 to 0.43 μm, classified based on aerodynamic diameter (d_ae) as fine PMs (0.43 μm ≤ d_ae < 2.1 μm) and coarse PMs (2.1 μm ≤ d_ae < 9.0 μm) were collected by cascade impactors (7 fractions) during smoke haze (SH) and non-smoke haze (NSH) seasons in urban and rural areas of Chiang Mai, Thailand. Their polycyclic aromatic hydrocarbons (PAHs) compositions were determined for respiratory health risk assessment. During SH episode, concentrations of SPMs and PAHs in the rural area were approximately two times higher than in the urban area and about 62-68% of the SPMs were fine particles. Conversely, during NSH season the concentrations in the urban area were higher due to traffic emission. The finest particle sizes (0.65-0.43 μm) contained the highest PAHs concentrations among the other PM sizes. Benzo[b]fluoranthene was a main PAH component found during SH season suggesting biomass burning is a major pollutant source. High molecular weight (5-6 rings) PAHs with high carcinogenicity were likely to concentrate in fine particles. Distribution patterns of SPMs and PAHs during SH season were bimodal with the highest peak at a fine size range (0.65-0.43 μm) and a small peak at a coarse size range (5.8-4.7 μm). Respiratory health risk was estimated based on toxicity equivalent concentrations of PAHs bound-SPMs and inhalation cancer risk (ICR). Relatively high ICR values (1.14 × 10^-4 (rural) and 6.80 × 10^-5 (urban)) were found during SH season in both areas, in which fine particles played an important role. It revealed that high concentration of fine particles in ambient air is related to high respiratory health risk due to high content of carcinogenic substances.

Fresh and aged PM2.5 and their ion composition in rural and urban atmospheres of Northern Thailand in relation to source identification

This study aims to investigate ion composition of PM_2.5 in various sites and seasons and to identify the main sources on spatial and temporal basis. PM_2.5 compositions of two urban and two rural areas in Northern Thailand in 2019 were investigated to distinguish urban traffic and rural open burning sources. During the burning season, average PM_2.5 concentrations in rural areas (104 ± 45 μg m^-3) were slightly higher than those in urban areas (94 ± 39 μg m^-3). Source identification of PM_2.5 by cluster analysis during burning season in urban sites and one rural site revealed mixed sources of aged aerosols from biomass burning, traffic and transboundary pollution, characterized by (NH₄)₂SO₄ and KNO₃. Only PM_2.5 in one rural area (Chiang Dao), where intense open burning activities observed, contained significant KCl level in addition to other compounds. KCl is being used as a tracer for fresh aerosols from biomass burning as opposes to KNO₃ for aged aerosols. It was found that KNO₃ proportion in total ions increased with PM_2.5 concentrations both in urban and rural areas, indicating prominent open burning influences in regional scale. Source identification in other seasons was more distinguishable between urban and rural areas, and more varied depending on local emissions. Urban PM_2.5 sources were secondary inorganic aerosols from traffic gas conversion in contrast with rural PM_2.5 which were mainly from biomass burning.

Tracers from Biomass Burning Emissions and Identification of Biomass Burning

The major organic compositions from biomass burning emissions are monosaccharide derivatives from the breakdown of cellulose, generally accompanied by small amounts of straight-chain, aliphatic, oxygenated compounds, and terpenoids from vegetation waxes, resins/gums, and other biopolymers. Levoglucosan from cellulose can be utilized as a specific or general indicator for biomass combustion emissions in aerosol samples. There are other important compounds, such as dehydroabietic acid, syringaldehyde, syringic acid, vanillic acid, vanillin, homovanillic acid, 4-hydroxybenzoic acid, and p-coumaric acid, which are additional key indicators of biomass burning. In this review, we will address these tracers from different types of biomass burning and the methods used to identify the sources in ambient aerosols. First, the methods of inferring biomass burning types by the ratio method are summarized, including levoglucosan/mannose, syringic acid/vanillic acid, levolgucosan/K+, vanillic acid/4-hydroxybenzoic acid, levoglucosan/OC, and levoglucosan/EC to infer the sources of biomass burning, such as crop residual burning, wheat burning, leaf burning, peatland fire, and forest fire in Asia. Second, we present the source tracer ratio methods that determine the biomass combustion types and their contributions. Finally, we introduce the PCA (Principal component analysis) and PMF (Positive matrix factor) methods to identify the type of biomass burning and its contributions according to emission factors of different species in various plants such as softwood, hardwood, and grass.

Chemical source profiles of fine particles for five different sources in Delhi

Increasing emissions from sources such as construction and burning of biomass from crop residues, roadside and municipal solid waste have led to a rapid increase in the atmospheric concentrations of fine particulate matter (≤2.5 μm; PM_2.5) over many Indian cities. Analyses of their chemical profiles are important for receptor models to accurately estimate the contributions from different sources. We have developed chemical source profiles for five important pollutant sources - construction (CON), paved road dust (PRD), roadside biomass burning (RBB), solid waste burning (SWB), and crop residue burning (CPB) - during three intensive campaigns (winter, summer and post-monsoon) in and around Delhi. We obtained chemical characterisations of source profiles incorporating carbonaceous material such as organic carbon (OC) and elemental carbon (EC), water-soluble ions (F^-, Cl^-, NO₂^-, NO₃^-, SO₄^2-, PO₄^3-, Na⁺ and NH₄⁺), and elements (Mg, Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Br, Rb, Sr, Ba, and Pb). CON was dominated by the most abundant elements, K, Si, Fe, Al, and Ca. PRD was also dominated by crustal elements, accounting for 91% of the total analysed elements. RBB, SWB and CPB profiles were dominated by organic matter, which accounted for 94%, 86.2% and 86% of the total PM_2.5, respectively. The database of PM emission profiles developed from the sources investigated can be used to assist source apportionment studies for accurate quantification of the causes of air pollution and hence assist governmental bodies in formulating relevant countermeasures.

PM10 chemical composition at a residential site in the western mediterranean: Estimation of the contribution of biomass burning from levoglucosan and its isomers

The composition of PM₁₀, including molecular markers of biomass burning (levoglucosan, mannosan and galactosan), was determined at a residential site in southeastern Spain during winter and early spring. The average PM₁₀ concentration was 25.0 μg m^-3, being organic carbon (OC, 6.77 μg m^-3), NO₃^- (2.02 μg m^-3), SO₄^2- (1.36 μg m^-3) and Ca²⁺ (1.01 μg m^-3) the main components. Levoglucosan was the dominant anhydrosugar (143 ng m^-3), accounting for 81% of the total concentration of monosaccharide anhydrides. The average contribution of biomass combustion to OC, estimated from the levoglucosan data, was 23%. This value agreed well with that calculated by Positive Matrix Factorization (PMF, 25%). The PMF model resolved six factors that were assigned to road traffic (28%), secondary aerosols (27%), soil dust (14%), fresh sea salt (13%), aged sea salt (10%) and biomass burning (8%). This model was used to estimate the OC/Levoglucosan and PM₁₀/Levoglucosan emission ratios for the study area.

Brown carbon light absorption over an urban environment in northern peninsular Southeast Asia

Light-absorbing organic carbon (or brown carbon, BrC) has been recognized as a critical driver in regional-to-global climate change on account of its significant contribution to light absorption. BrC sources vary from primary combustion processes (burning of biomass, biofuel, and fossil fuel) to secondary formation in the atmosphere. This paper investigated the light-absorbing properties of BrC such as site-specific mass absorption cross-section (MAC_BrC), absorption Ångström exponent (AAE_BrC), and the absorbing component of the refractive index (k_BrC) by using light absorption measurements from a 7-wavelength aethalometer over an urban environment of Chiang Mai, Thailand in northern peninsular Southeast Asia (PSEA), from March to April 2016. The contribution of BrC to total aerosol absorption (mean ± SD) was 46 ± 9%, 29 ± 7%, 24 ± 6%, 20 ± 4%, and 15 ± 3% at 370, 470, 520, 590, and 660 nm, respectively, highlighting the significant influence of BrC absorption on the radiative imbalance over northern PSEA. Strong and significant associations between BrC light absorption and biomass-burning (BB) organic tracers highlighted the influence of primary BB emissions. The median MAC_BrC and k_BrC values at 370 nm were 2.4 m² g^-1 and 0.12, respectively. The fractional contribution of solar radiation absorbed by BrC relative to BC (mean ± SD) in the 370-950 nm range was estimated to be 34 ± 7%, which can significantly influence the regional radiation budget and consequently atmospheric photochemistry. This study provides valuable information to understand BrC absorption over northern PSEA and can be used in model simulations to reassess the regional climatic impact with greater accuracy.

Biomass burning-derived airborne particulate matter in Southeast Asia: A critical review

Smoke haze episodes, resulting from uncontrolled biomass burning (BB) including forest and peat fires, continue to occur in Southeast Asia (SEA), affecting air quality, atmospheric visibility, climate, ecosystems, hydrologic cycle and human health. The pollutant of major concern in smoke haze is airborne particulate matter (PM). A number of fundamental laboratory, field and modeling studies have been conducted in SEA from 2010 to 2020 to investigate potential environmental and health impacts of BB-induced PM. The goal of this review is to bring together the most recent developments in our understanding of various aspects of BB-derived PM based on 127 research articles published from 2010 to 2020, which have not been conveyed in previous reviews. Specifically, this paper discusses the physical, chemical, toxicological and radiative properties of BB-derived PM. It also provides insights into the environmental and health impacts of BB-derived PM, summarizes the approaches taken to do the source apportionment of PM during BB events and discusses the mitigation of exposure to BB-derived PM. Suggestions for future research priorities are outlined. Policies needed to prevent future BB events in the SEA region are highlighted.

Temporal variation characteristics and source apportionment of metal elements in PM2.5 in urban Beijing during 2018-2019

To explore high-resolution temporal variation characteristics of atmospheric metal elements concentration and more accurate pollution sources apportionment, online monitoring of metal elements in PM_2.5 with 1-h time resolution was conducted in Beijing from August 22, 2018 to August 21, 2019. Concentration of 18 elements varied between detection limit (ranging from 0.1 to 100 ng/m³) and nearly 25 μg/m³. Si, Fe, Ca, K and Al represented major elements and accounted for 93.47% of total concentration during the study period. Compared with previous studies, airborne metal pollution in Beijing has improved significantly which thanks to strict comprehensive control measures under the Clean Air Action Plan since 2013. Almost all elements present higher concentrations on weekdays than weekends, while concentrations of elements associated with dust sources during holidays are higher than those in working days after the morning peak, and there is almost no concentration difference in the evening peak period. Soil and dust, vehicle non-exhaust emissions, biomass, industrial processes and fuel combustion were apportioned as main sources of atmospheric metal pollution, accounting for 63.6%, 18.4%, 16.8%, 1.0% and 0.18%, respectively. Furthermore, main occurrence season of metal pollution is judged by characteristic radar chart of varied metal elements proposed for the first time in this study, for example, fuel combustion type pollution mainly occurs in winter and spring. Results of 72-h backward trajectory analysis of air masses showed that, except for local emissions, atmospheric metal pollution in Beijing is also affected by regional transport from Inner Mongolia, Hebei, the Bohai Sea and Heilongjiang.

Biogenic secondary organic aerosols: A review on formation mechanism, analytical challenges and environmental impacts

The review initiates with current state of information on the atmospheric reaction mechanism of biogenic volatile organic compounds (BVOCs) and its fate in the atmosphere. The plants release BVOCs, i.e., isoprene, monoterpenes, and sesquiterpenes, which form secondary organic aerosols (SOA) upon oxidation. These oxidation reactions are primarily influenced by solar radiations along with other meteorological parameters viz.; temperature and relative humidity, therefore, the chemistry behind SOA formation is different during day than the night time. The review throws light upon the day and nighttime formation mechanism of SOA, recent advancements in the analytical techniques available for the measurements, and its impact on the environment. Studies have revealed that day time SOA formation is dominated by OH and O₃, however, NOx initiated SOA production is dominated during night. The formation mechanism addresses that the gaseous products of VOCs are firstly formed and then partitioned over the pre-existing particles. New particle formation and biomass-derived aerosols are found to be responsible for enhanced SOA formation. 2-Dimensional gas chromatography-mass spectrometer (2D-GC/MS) is observed to be best for the analysis of organic aerosols. Radiative forcing (RF) SOA is observed to be a useful parameter to evaluate the environmental impacts of SOA and reviewed studies have shown mean RF in the ranges of -0.27 to +0.20 W m^-2.

COVID-19 lockdowns reduce the Black carbon and polycyclic aromatic hydrocarbons of the Asian atmosphere: source apportionment and health hazard evaluation

ABSTRACT

The entire world is affected by Coronavirus disease (COVID-19), which is spreading worldwide in a short time. India is one of the countries which is affected most, therefore, the Government of India has implemented several lockdowns in the entire country from April 25, 2020. We studied air pollutants (i.e., PM_2.5, Black Carbon (BC), and Polycyclic Aromatic Hydrocarbons (PAHs) level, and observed significantly sudden reduced. In India, most of the anthropogenic activities completely stopped. Therefore, we studied the levels of BC, PAHs and PM_2.5 concentrations, their sources apportion, and health risk assessment during normal days, lockdown (from lockdown 1.0 to lockdown 4.0) and unlock down 1.0 situation at Sakchi, Jamshedpur city. It was observed that lockdowns and unlock down situations BC, PAHs and PM_2.5 concentrations were significantly lower than regular days. We applied the advanced air mass back trajectory (AMBT) model to locate airborne particulate matter dispersal from different directions to strengthen the new result. The diagnostic ratio analyses of BC shows that wood burning contribution was too high during the lockdown situations. However, during normal days, the PAHs source profile was dedicated toward biomass, coal burning, and vehicle emission as primary sources of PAHs. During the lockdown period, emission from biomass and coal burning was a significant contributor to PAHs. The summaries of health risk assessment of BC quantified an equal number of passively smoked cigarettes (PSC) for an individual situation was studied. This study focuses on the overall climate impact of pandemic situations.

Emission reduction of black carbon and polycyclic aromatic hydrocarbons during COVID-19 pandemic lockdown

The global pandemic COVID-19 necessitated various responses throughout the world, including social distancing, use of mask, and complete lockdown. While these measures helped prevent the community spread of the virus, the resulting environmental benefits of lockdown remained mostly unnoticed. While many studies documented improvements in air quality index, very few have explored the reduction in black carbon (BC) aerosols and polycyclic aromatic hydrocarbons (PAHs) concentrations due to lockdown. In this study, we evaluated the changes in concentrations of BC, PAHs, and PM_2.5 before and during the lockdown period. Our results show that lockdown resulted in a significant reduction in concentrations of these pollutants. The average mass concentration of BC, PAHs, and PM_2.5 before the lockdown was 11.71 ± 3.33 μgm^-3, 108.71 ± 27.77 ngm^-3, and 147.65 ± 41.77 μgm^-3, respectively. During the lockdown period, the concentration of BC, PAHs, and PM_2.5 was 2.46 ± 0.95 μgm^-3, 23.19 ± 11.21 ngm^-3, and 50.31 ± 11.95 μgm^-3, respectively. The diagnostic ratio analysis for source apportionment showed changes in the emission sources before and during the lockdown. The primary sources of PAHs emissions before the lockdown were biomass, coal combustion, and vehicular traffic, while during the lockdown, PAHs emissions were primarily from the combustion of biomass and coal. Similarly, before the lockdown, the BC mass concentrations came from fossil-fuel and wood-burning, while during the lockdown period, most of the BC mass concentration came from wood-burning. Human health risk assessment demonstrated a significant reduction in risk due to inhalation of PAHs and BC-contaminated air.

Characterising particulate matter source contributions in the pollution control zone of mining and related industries using bivariate statistical techniques

Na Phra Lan Subdistrict is a pollution control zone with the highest PM10 level in Thailand. Major mobile and industrial sources in the area are related to stone crushing, quarrying and mining. This study used statistical techniques to investigate the potential sources influencing high PM10 levels in Na Phra Lan. Hourly PM10 data and related parameters (PM2.5, PMcoarse and NOx) from 2014-2017 were analysed using time series, bivariate polar plot and conditional bivariate probability function (CBPF). Results of diurnal variation revealed two peaks of PM10 levels from 06:00-10:00 and 19:00-23:00 every month. For seasonal variation, high PM10 concentrations were found from October to February associated with the cool and dry weather during these months. The bivariate polar plot and CBPF confirmed two potential sources, i.e., resuspended dust from mobile sources close to the air quality monitoring station (receptor) and industrial sources of mining, quarrying and stone crushing far from the station on the northeast side. While the industrial source areas played a role in background PM10 concentrations, the influence of mobile sources increased the concentrations resulting in two PM10 peaks daily. From the study results, we proposed that countermeasure activities should focus on potential source areas, resuspended road dust from vehicles and the industrial sources related to quarrying and mining, rather than distributing equal attention to all sources.

Impact of transport of fine and ultrafine particles from open biomass burning on air quality during 2019 Bangkok haze episode

Transboundary and domestic aerosol transport during 2018-2019 affecting Bangkok air quality has been investigated. Physicochemical characteristics of size-segregated ambient particles down to nano-particles collected during 2017 non-haze and 2018-2019 haze periods were analyzed. The average PM_2.5 concentrations at KU and KMUTNB sites in Bangkok, Thailand during the haze periods were about 4 times higher than in non-haze periods. The highest average organic carbon and elemental carbon concentrations were 4.6 ± 2.1 µg/m³ and 1.0 ± 0.4 µg/m³, respectively, in PM_0.5-1.0 range at KU site. The values of OC/EC and char-EC/soot-EC ratios in accumulation mode particles suggested the significant influence of biomass burning, while the nuclei and coarse mode particles were from mixed sources. PAH concentrations during 2018-2019 haze period at KU and KMUTNB were 3.4 ± 0.9 ng/m³ and 1.8 ± 0.2 ng/m³, respectively. The PAH diagnostic ratio of PM_2.5 also suggested the main contributions were from biomass combustion. This is supported by the 48-hrs backward trajectory simulation. The higher PM_2.5 concentrations during 2018-2019 haze period are also associated with the meteorological conditions that induce thermal inversions and weak winds in the morning and evening. Average values of benzo(a)pyrene toxic equivalency quotient during haze period were about 3-6 times higher than during non-haze period. This should raise a concern of potential human health risk in Bangkok and vicinity exposing to fine and ultrafine particulate matters in addition to regular exposure to traffic emission.

Simultaneous determination of carcinogenic PAHs and levoglucosan bound to PM2.5 for assessment of health risk and pollution sources during a smoke haze period

Carcinogenic polycyclic aromatic hydrocarbons (cPAHs) in ambient PM_2.5 and a specific molecular marker of biomass burning, levoglucosan, are used to investigate the influence on public health of biomass burning. In this work, we present an effective method for one-time analysis of cPAHs and levoglucosan by GC-MS without derivatization. The method was applied for the analysis of PM_2.5 samples (64.3 ± 17.6 μg m^-3, n = 57) collected during a smoke haze period in Chiang Mai, Thailand. Levoglucosan was analyzed by using both the developed method (GC-MS) and a reference method (HPAEC-PAD) for comparison. Its average concentration obtained from GC-MS (0.31 ± 0.21 μg m^-3) was about 4 times less than the concentration obtained from the reference method (1.22 ± 0.76 μg m^-3). Therefore, a correcting factor (CF = 4) was used as a multiplying factor, to obtain a comparative value (1.23 ± 0.86 μg m^-3). The average concentration of cPAHs found in PM_2.5 samples was 5.88 ± 1.97 ng m^-3 with the highest value of 10.86 ng m^-3 indicating medium to high cancer risk due to PAHs exposure when referring to values of toxicity equivalence and inhalation cancer risk. Diagnostic ratios of BaA/(BaA + CHR) (0.48 ± 0.04) and IND/(IND + BPER) (0.58 ± 0.04) and strong correlations between PM_2.5, levoglucosan and cPAHs concentrations implied that the major source of air pollution in the study period was biomass burning. PM_2.5 concentration as a pollution indicator was labelled as BB-low, BB-medium, BB-high or BB-extreme; <50, 50-75, 75-100 and > 100 μg m^-3, respectively. The levoglucosan and cPAHs concentration during BB-extreme pollution was 4.3 times and 2.34 times, respectively, that during BB-low pollution, and the correlation coefficient (r) between the concentrations of levoglucosan and cPAHs was as high as 0.987, indicating that the more intense the burning of biomass, the higher the carcinogenic risk in the urban air.

Physicochemical and toxicological characteristics of nanoparticles in aerosols in southern Thailand during recent haze episodes in lower southeast Asia

Transboundary haze from biomass burning is one of the most important air pollutions in Southeast Asia. The most recent serious haze episode occurred in 2015. Southern Thailand was affected by the haze during September to October when the particulate matter concentration hit a record high. We investigated physical and chemical characteristics of aerosols, including concentration and aerosol size distribution down to sub-micron sizes during haze episodes in 2013 and 2015 and, for reference, an insignificant haze period in 2017. The highest total suspended particulates and PM₁₀ levels in Hat Yai city were 340.1 and 322.5 µg/m³. The mass fractions were nanoparticles (< 100 nm) 3.1%-14.8% and fine particles (< 1 µm) 54.6%-59.1%. Polycyclic aromatic hydrocarbon size distributions in haze periods peaked at 0.75 µm and the concentrations are 2-30 times higher than the normal period. High molecular weight (4-6 ring) PAHs during the haze episode contribute to about 56.7%-88.0% for nanoparticles. The average values of benzo(a)pyrene toxic equivalency quotient were 3.34±2.54ng/m³ in the 2015 haze period but only 0.89±0.17 ng/m³ in 2017. It is clear that particles smaller than 1 µm, were highly toxic. Nanoparticles contributed 19.4%-26.0% of total BaP-TEQ, whereas the mass fraction is 13.1%-14.8%. Thus the nanoparticles were more carcinogenic and can cause greater health effect than larger particles. The fraction of BaP-TEQ for nanoparticles during 2017 non-haze period was nearly the same, while the mass fraction was lower. This indicates that nanoparticles are the significant source of carcinogenic aerosols both during haze and non-haze periods.

Source apportionment of PM10 by positive matrix factorization model at a source region of biomass burning

To determine the contribution of different particulate sources in PM₁₀ mass concentration at semi urban site, source apportionment study was carried out from 7 May 2015 to 9 June 2016. PM₁₀ samples were analyzed for 18 species (NO₃^-, SO₄^2-, Cl^-,Na⁺, K⁺, Al, Ti, Mn, Fe, Mg, V, Ni, Cu, Zn, Ba, Pb, Cr, Ca). The study was specifically designed to apportion the sources of air pollution where main exposure is from crop residue burning. The particulate matter (PM₁₀) samples were analyzed for mass and chemical composition, with Potassium as biomarker for crop residue burning. Sulfate SO₄^2-) and potassium ion (K⁺) species dominated the concentration of characterized species. K⁺ and Cl^- were identified as reliable markers for crop residue burning while Zn, Pb, Al, Ni and Cu were identified as markers for vehicular exhaust. The results of Positive matrix factorization (PMF) model gives the five major sources as probable sources of PM₁₀ pollution. The highest contribution in PM₁₀ mass concentration was found to be sulfate (24.39 ± 10.42), and potassium (24.02 ± 09.56) and chloride (07.07 ± 05.47), which combined accounts for nearly 60% of the total PM₁₀ mass fraction. The highest source contribution was from Industrial emission source (22.9%), with almost same contribution from biomass burning (21.2%), and resuspended dust (20.7%) and followed by vehicular emissions (19.0%) and least from secondary aerosols (16.2%).

Quantifying the contributions of local emissions and regional transport to elemental carbon in Thailand

We used the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) to simulate elemental carbon (EC) concentrations in Thailand in 2017. The goals were to quantify the respective contributions of local emissions and regional transport outside Thailand to EC pollution in Thailand, and to identify the most effective emission control strategy for decreasing EC pollution. The simulated EC concentrations in Chiang Mai, Bangkok, and Phuket were comparable with the observation data. The correlation coefficient between the simulated and observed EC concentrations was 0.84, providing a good basis for evaluating EC sources in Thailand. The simulated mean EC concentration over the whole country was the highest (1.38 μg m^-3) in spring, and the lowest (0.51 μg m^-3) in summer. We conducted several sensitivity simulations to evaluate EC sources. Local emissions (including anthropogenic and biomass burning emissions) and regional transport outside Thailand contributed 81.2% and 18.8% to the annual mean EC concentrations, respectively, indicating that local sources played the dominant role for EC pollution in Thailand. Among the local sources, anthropogenic emissions (including the industry, power plant, residential, and transportation sectors) and biomass burning contributed 75.1% and 6.1% to the annual mean EC concentrations, respectively. As the anthropogenic emissions dominated the EC pollution, we performed four sensitivity simulations by reducing 30% of the emissions from each of the industry, power plant, residential, and transportation sectors in Thailand. The results indicated that controlling transportation emissions in Thailand was the most effective way in reducing the EC pollution. The 30% reduction of transportation emissions decreased the annual mean EC concentrations by 12.1%. In contrast, 30% reductions of the residential, industry, and power plant emissions caused 8.4%, 6.4%, and 4.0% decreases in the annual mean EC concentrations, respectively. The model results could potentially provide useful information for air pollution control strategies in Thailand.

Satellite measurements of aerosol optical depth and carbon monoxide and comparison with ground data

Satellite data of aerosol optical depths (AODs) from the moderate resolution imaging spectroradiometer (MODIS) and carbon monoxide (CO) columns from the measurements of pollution in the troposphere (MOPITT) were collected for the study in Northern Thailand. Comparative analyses were conducted of MODIS (Terra and Aqua) AODs with ground particulate matter with diameter below 10 microns (PM₁₀) concentrations and MOPITT CO surface/total columns with ground CO concentrations for 2014-2017. Temporal variations in both the satellite and ground datasets were in good agreement. High levels of air pollutants were common during March-April. The annual analysis of both satellite and ground datasets revealed the highest levels of air pollutants in 2016 and the lowest levels in 2017. The AODs and PM₁₀ concentrations were at higher levels in the morning than in the afternoon. The comparison between satellite products showed that AODs correlated better with the CO total columns than the CO surface columns. The regression analysis presented better performance of Aqua AODs-PM₁₀ than Terra AODs-PM₁₀ with correlation coefficients (r) of 0.72-0.83 and 0.57-0.79, respectively. Ground CO concentrations correlated better with MOPITT CO surface columns (r = 0.65-0.73) than with CO total columns (r = 0.56-0.72). The r values of satellite and ground datasets were greatest when the analysis was restricted to November-March (dry weather periods with possible low mixing height (MH)). Overall, the results suggested that the relationships between satellite and ground data can be used to develop predictive models for ground PM₁₀ and CO in northern Thailand, particularly during air pollution episodes located where ground monitoring stations are limited.

Size-fractionated carbonaceous aerosols down to PM0.1 in southern Thailand: Local and long-range transport effects

In this study, size-fractionated particulate matters (PM) down to ultrafine (PM_0.1) particles were collected using a cascade air sampler with a PM_0.1 stage, in Hat Yai city, Songkhla province, southern Thailand during the year 2018. The particle-bound carbonaceous aerosols (CA) as elemental carbon (EC) and organic carbon (OC) were quantified with the thermal/optical reflectance method following the IMPROVE_TOR protocol. The concentrations of different temperature carbon fractions (OC1-OC4, EC1-EC3 and PyO) in the size-fractionated PM were evaluated to discern OC and EC correlations as well as those between char-EC and soot-EC. The results showed that biomass burning, motor vehicle, and secondary organic aerosols (SOC) all contributed to the size-fractionated PM. The OC/EC ratios ranged from 2.90 to 4.30 over the year, with the ratios of PM_2.5-10 being the highest, except during the open biomass burning period. The concentration of CA was found to increase during the pre-monsoon season and had its peak value in the PM_0.5-1.0 fraction. The long-range transport of PMs from Indonesia, southwest of Thailand toward southern Thailand became more obvious during the pre-monsoon season. Transported plumes from biomass burning in Indonesia may increase the concentration of OC and EC both in the fine (PM_0.5-1.0 and PM_1.0-2.5) and coarse (PM_2.5-10 and PM_>10) fractions. The OC fraction in PM_0.1 was also shown to be significantly affected by the transported plumes during the pre-monsoon season. Good OC and EC correlations (R² = 0.824-0.915) in the fine particle fractions indicated that they had common sources such as fossil fuel combustion. However, the lower and moderate correlations (R² = 0.093-0.678) among the coarser particles suggesting that they have a more complex pattern of emission sources during the dry and monsoon seasons. This indicates the importance of focusing emission control strategies on different PM particle sizes in southern Thailand.

Black carbon over an urban atmosphere in northern peninsular Southeast Asia: Characteristics, source apportionment, and associated health risks

Black carbon (BC) has been demonstrated to pose significant negative impacts on climate and human health. Equivalent BC (EBC) measurements were conducted using a 7-wavelength aethalometer, from March to May 2016, over an urban atmosphere, viz., Chiang Mai (98.957°E, 18.795°N, 373 m above sea level), Thailand in northern peninsular Southeast Asia. Daily variations in aerosol light absorption were mainly governed by open fire activities in the region. The mean mass-specific absorption cross-section (MAC) value of EBC at 880 nm was estimated to be 9.3 m² g^-1. The median EBC mass concentration was the highest in March (3.3 μg m^-3) due to biomass-burning (comprised of forest fire and agricultural burning) emissions accompanied by urban air pollution within the planetary boundary layer under favorable meteorological conditions. Daily mean absorption Ångström exponent (AAE_470/950) varied between 1.3 and 1.7 and could be due to variations in EBC emission sources and atmospheric mixing processes. EBC source apportionment results revealed that biomass-burning contributed significantly more to total EBC concentrations (34-92%) as compared to fossil-fuel (traffic emissions). Health risk estimates of EBC in relation to different health outcomes were assessed in terms of passive cigarette equivalence, highlighting the considerable health effects associated with exposure to EBC levels. As a necessary action, the reduction of EBC emissions would promote considerable climate and health co-benefits.

Development of Multiple Linear Regression for Particulate Matter (PM10) Forecasting during Episodic Transboundary Haze Event in Malaysia

Malaysia has been facing transboundary haze events every year in which the air contains particulate matter, particularly PM10, which affects human health and the environment. Therefore, it is crucial to develop a PM10 forecasting model for early information and warning alerts to the responsible parties in order for them to mitigate and plan precautionary measures during such events. Therefore, this study aimed to develop and compare the best-fitted model for PM10 prediction from the first hour until the next three hours during transboundary haze events. The air pollution data acquired from the Malaysian Department of Environment spanned from the years 2005 until 2014 (excluding years 2007–2009), which included particulate matter (PM10), ozone (O3), nitrogen oxide (NO), nitrogen dioxide (NO), carbon monoxide (CO), sulfur dioxide (SO2), wind speed (WS), ambient temperature (T), and relative humidity (RH) on an hourly basis. Three different stepwise Multiple Linear Regression (MLR) models for predicting the PM10 concentration were then developed based on three different prediction hours, namely t+1, t+2, and t+3. The PM10, t+1 model was the best MLR model to predict PM10 during transboundary haze events compared to PM10,.t+2 and PM10,t+3 models, having the lowest percentage of total error (28%) and the highest accuracy of 46%. A better prediction and explanation of PM10 concentration will help the authorities in getting early information for preserving the air quality, especially during transboundary haze episodes.

Detection of PM2.5 plume movement from IoT ground level monitoring data

In this study, we analysed a data set from 10 low-cost PM_2.5 sensors using the Internet of Things (IoT) for air quality monitoring in Mae Sot, which is one of the most vulnerable areas for high PM_2.5 concentration in Thailand, during the 2018 burning season. Our objectives were to understand the nature of the plume movement and to investigate possibilities of adopting IoT sensors for near real-time forecasting of PM_2.5 concentrations. Sensor data including PM_2.5 and meteorological parameters (wind speed and direction) were collected online every 2 min where data were grouped into four zones and averaged every 15 min interval. Results of diurnal profile plot revealed that PM_2.5 concentrations were high around early to late morning (3:00-9:00) and gradually reduced till the rest of the day. During the biomass burning period, maximum daily average concentration recorded by the sensors was 280 μg/m³ at Thai Samakkhi while the minimum was 13 μg/m³ at Mae Sot. Lag time concentrations, attributed by biomass burning (hotspots), significantly influenced the formation of PM_2.5 while the disappearance of PM_2.5 was found to be influenced by moderate wind speed. The PM_2.5 concentrations of the next 15 min at the downwind zone (MG) were predicted using lag time concentrations with different wind categories. The next 15 min predictions of PM_2.5 at MG were found to be mainly influenced by its lag time concentrations (MG_Lag); with higher wind speed, however, the lag time concentrations from the upwind zones (MS_Lag and TS_Lag) started to show more influence. From this study, we have found that low-cost IoT sensors provide not only real-time monitoring information but also demonstrate great potential as an effective tool to understand the PM_2.5 plume movement with temporal variation and geo-specific location.

Emissions of pollutant gases, fine particulate matters and their significant tracers from biomass burning in an open-system combustion chamber

An open-system combustion chamber was designed and constructed for simulation of burning of various biomass types to estimate emission factors of pollutant gases, fine particulate matters and their composition to find out significant tracers. Rice straw (RS), maize residues (MR) and forest leaf litters (FLL) from mixed deciduous forest (MDF) and dry dipterocarp forest (DDF) were collected from various places in Northern Thailand based on land-use types. Approximately 1 kg of air-dried biomass sample was burned in the chamber, PM_2.5 were collected. CO₂ dominated during the flaming state while CO is predominant in the smoldering state. The highest EF_PM2.5 was obtained from MDF burning (4.38 ± 2.99 g kg^-1), while the lowest value was from MR burning (2.15 ± 0.95 g kg^-1). Among water soluble ions, K⁺ (biomass burning (BB) tracer) was the most abundant species in PM_2.5 followed by Cl^- and SO₄^2-. The average EF_K⁺ from the burning of agricultural biomass was significantly higher than the burning of FLL. Scatter plot of K⁺/SO₄^2- versus K⁺/Cl^- can be used to distinguish between agricultural crop residues and FLL burning. Levoglucosan (BB tracer) was a dominant species among anhydrosugars and also a major component found in FLL burning. The ratios of levoglucosan/K⁺ and levoglucosan/mannosan obtained from forest and agricultural waste burnings were significantly different, therefore they can be used for BB source identification.