An Investigation of Carbonaceous Components and Source Identification of Ultrafine Particulate Matter in the Atmosphere of the Southern Region of Thailand

Transboundary haze from peatland fires and local source-derived PM2.5 in Southern Thailand

The Indo-Gangetic Plains (IGP) experience high levels of airborne particulate matter (PM), especially during the dry season. Contributing to PM are natural and anthropogenic emissions and the atmospheric transformation of gases to form particles. Regional smog events occur frequently during wintertime and provide an atmospheric medium for aerosol processing. Here, we investigate the chemical composition and sources of PM at a representative site in the northern IGP during the second Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE 2). In Lumbini, Nepal, the 24 h average PM2.5 and PM10 concentrations ranged 48–295 and 60–343 μg m–3, respectively, from December 20, 2017, to January 1, 2018. On average (± standard deviation), PM2.5 was composed of 39 ± 7% organic carbon (OC), 5 ± 2% elemental carbon (EC), and 20 ± 6% secondary inorganic ions (ammonium, nitrate, and sulfate), 2.0% chloride, and 1.3% potassium. Biomass burning was a major PM source, indicated by a median levoglucosan concentration of 3.5 μg m–3. Secondary organic aerosol (SOA) derived from biomass burning was indicated by high concentrations of nitromonoaromatic compounds (e.g., 4-nitrocatechol peaking at 435 ng m–3). During periods of fog, characterized by high relative humidity (RH) and relatively low solar radiation, nitroaromatic concentrations dropped despite levoglucosan remaining high, indicating that their formation was suppressed. Chemical signatures of SOA indicated that volatile organic compound (VOC) precursors were primarily combustion-derived, with small contributions from biogenic VOC. Through molecular markers and chemical mass balance (CMB) modeling, sources of PM2.5 OC were identified as cow dung burning (24 ± 16%), other biomass burning (20 ± 7%), plastic/garbage burning (4.7 ± 3.2%), vehicle emissions (3.1 ± 1.4%), coal combustion (0.3 ± 0.2%), and SOA from monoaromatic VOC (4.1 ± 0.8%), diaromatic VOC (8.9 ± 4.0%), cresol (0.3 ± 0.4%), isoprene (0.4 ± 0.2%), monoterpenes (1.5 ± 0.6%), and sesquiterpenes (3.2 ± 0.7%). Understanding the levels of PM in Lumbini, along with its chemical composition and sources of OC, contributes to a better understanding of regional air quality episodes in the IGP.

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

Source apportionment of organic carbon in Centreville, AL using organosulfates in organic tracer-based positive matrix factorization

This study focuses on improving source apportionment of carbonaceous aerosol in South Asia and consists of three parts: (1) development of novel molecular marker–based profiles for real‐world biofuel combustion, (2) application of these profiles to a year‐long data set, and (3) evaluation of profiles by an in‐depth sensitivity analysis. Emissions profiles for biomass fuels were developed through source testing of a residential stove commonly used in South Asia. Wood fuels were combusted at high and low rates, which corresponded to source profiles high in organic carbon (OC) or high in elemental carbon (EC), respectively. Crop wastes common to the region, including rice straw, mustard stalk, jute stalk, soybean stalk, and animal residue burnings, were also characterized. Biofuel profiles were used in a source apportionment study of OC and EC in Godavari, Nepal. This site is located in the foothills of the Himalayas and was selected for its well‐mixed and regionally impacted air masses. At Godavari, daily samples of fine particulate matter (PM2.5) were collected throughout the year of 2006, and the annual trends in particulate mass, OC, and EC followed the occurrence of a regional haze in South Asia. Maximum concentrations occurred during the dry winter season and minimum concentrations occurred during the summer monsoon season. Specific organic compounds unique to aerosol sources, molecular markers, were measured in monthly composite samples. These markers implicated motor vehicles, coal combustion, biomass burning, cow dung burning, vegetative detritus, and secondary organic aerosol as sources of carbonaceous aerosol. A molecular marker–based chemical mass balance (CMB) model provided a quantitative assessment of primary source contributions to carbonaceous aerosol. The new profiles were compared to widely used biomass burning profiles from the literature in a sensitivity analysis. This analysis indicated a high degree of stability in estimates of source contributions to OC when different biomass profiles were used. The majority of OC was unapportioned to primary sources and was estimated to be of secondary origin, while biomass combustion was the next‐largest source of OC. The CMB apportionment of EC to primary sources was unstable due to the diversity of biomass burning conditions in the region. The model results suggested that biomass burning and fossil fuel were important contributors to EC, but could not reconcile their relative contributions.

The Southern Oxidant and Aerosol Study (SOAS) was a large field campaign during June-July 2013 in the southeast USA (Hidy et al., 2014; Hu et al., 2015; Carlton et al., 2018). Vast forested areas emitting large amounts of organic compounds and proximity to metropolitan areas present an ideal environment to investigate the influence of anthropogenic emissions on the biogenic secondary organic aerosol (SOA) formation. The main site of this study, located in rural Centreville, AL, was equipped with a wide variety of state-of-the-art analytical instruments. This project focuses on the source apportionment of the organic carbon (OC) fraction of ambient aerosol samples, through combination of radiocarbon (14C) data with positive matrix factorization information from online aerosol mass spectrometry measurements (AMS-PMF). Analysis of the long-lived radioactive isotope 14C is a unique source apportionment tool: it unambiguously separates fossil from non-fossil sources, as 14C has completely decayed in fossil fuels, whereas modern materials have the contemporary radiocarbon level (Szidat, 2009). 14C was measured for total carbon (TC) and elemental carbon (EC) (Zhang et al., 2012) from quartz filters that were collected at Centreville with daily resolution. This allowed the apportionment of fossil vs. non-fossil sources for EC and OC. Although OC mainly originated from non-fossil sources, a certain fraction was attributed to emissions from fossil sources. These results were compared with AMS-PMF data from a high-resolution time-of-flight AMS (Hu et al., 2015), which identified six different factors, i.e. biomass burning organic aerosol (BBOA), SOA formed through direct condensation of low-volatility oxidation products from isoprene (ISOPOOH-SOA), isoprene epoxydiols-derived SOA (IEPOX-SOA), low-oxidized oxygenated organic aerosol I, attributed to mostly biogenic sources (LO OOAI), low-oxidized OOA II, attributed mostly to anthropogenic sources (LO-OOAII) and more-oxidized OOA (MO-OOA). On average, the less well-understood fractions LO-OOAI, LO-OOAII and MO-OOA comprise ~3/4 of the total organic aerosol mass. 14C analysis of EC enables the distinction of sources of this carbonaceous aerosol fraction between fossil-fuel combustion (mainly from traffic) and biomass burning. It indicated a larger contribution from biomass burning compared to other source apportionment techniques or results from bottom-up emission inventories. The combination of 14C and AMS-PMF analysis provides the potential to apportion fossil vs. non-fossil sources for components for which the non-fossil fraction cannot by analyzed directly, such as SOA (Zotter et al., 2014). In this work, we present such results for the SOAS campaign based on Markov chain Monte Carlo calculations to gain more insight into the sources of SOA precursors. LO-OOAI, LO-OOAII and MO-OOA reveal different contributions of fossil and non-fossil sources, which allows a better understanding of these AMS-PMF factors. Carlton, A. M., et al. (2018), Bull. Am. Met. Soc., in press. Hidy, G. M., et al. (2014), Atmos. Chem. Phys., 14, 11893-11914. Hu, W. W., et al. (2015), Atmos. Chem. Phys., 15, 11807-11833. Szidat, S. (2009), Chimia, 63, 157-161. Zhang, Y. L., et al., (2012) Atmos. Chem. Phys., 12 (22), 10841-10856. Zotter, P., et al. (2014), J. Geophys. Res. Atmos., 119, 6818–6835.

Characteristics and Sources of Organic Aerosol in PM2.5 at Yangbajing in Tibetan Plateau

Receptor modeling of PM 2.5, PM 10 and TSP in different seasons and long-range transport analysis at a coastal site of Tianjin, China

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

Characteristics of trace elements bound to ambient nanoparticles (PM0.1) and a health risk assessment in southern Thailand

The chemical and optical properties and sources of atmospheric PM2.5 humic-like substances (HULIS) were investigated from October to December 2016 in both industrial and suburban areas in Changzhou, China, during polluted and fair days. The average PM2.5 concentration in the industrial region was 113.06 (±64.3) μg m−3, higher than 85.27 (±41.56) μg m−3 at the suburban site. The frequency of polluted days was significantly higher in the industrial region. In contrast, the chemical compositions of PM2.5 at the two sampling sites exhibited no statistically significant differences. Rapidly increased secondary inorganic ions (SNA = NH4+ + SO42− + NO3−) concentrations suggested secondary formation played an important role in haze formation. The daily mean concentration of humic-like substance (HULIS) was 1.8–1.9 times that of HULIS-C (the carbon content of HULIS). Our results showed that HULIS accounted for a considerable fraction of PM2.5 (industrial region: 6.3% vs. suburban region: 9.4%). There were no large differences in the mass ratios of HULIS-C/WSOC at the two sites (46% in the industrial region and 52% in the suburban region). On average, suburban HULIS-C constituted 35.1% of organic carbon (OC), higher than that (21.1%) in the industrial region. Based on different MAE (mass absorption efficiency) values under different pollution levels, we can infer that the optical properties of HULIS varied with PM levels. Moreover, our results showed no distinct difference in E2/E3 (the ratio of light absorbance at 250 nm to that at 365 nm) and AAE300–400 (Absorption Angstrom Exponent at 300–400 nm) for HULIS and WSOC. the MAE365 (MAE at 365 nm) value of HULIS-C was different under three PM2.5 levels (low: PM2.5 < 75 μg m−3, moderate: PM2.5 = 75–150 μg m−3, high: PM2.5 > 150 μg m−3), with the highest MAE365 value on polluted days in the industrial region. Strong correlations between HULIS-C and SNA revealed that HULIS might be contributed from secondary formation at both sites. In addition, good correlations between HULIS-C with K+ in the industrial region implied the importance of biomass burning to PM2.5-bound HULIS. Three common sources of HULIS-C (i.e., vehicle emissions, biomass burning, and secondary aerosols) were identified by positive matrix factorization (PMF) for both sites, but the contributions were different, with the largest contribution from biomass burning in the industrial region and secondary sources in the suburban region, respectively. The findings presented here are important in understanding PM2.5 HULIS chemistry and are valuable for future air pollution control measures.

The growth of secondary organic aerosols (SOA) is a vital cause of the outbreaks of winter haze in North China. Intermediate volatile organic compounds (IVOCs) are important precursors of SOA. Therefore, the chemical characteristics, source, and SOA production of IVOCs during haze episodes have attracted much attention. Hourly time resolution IVOC samples during two haze episodes collected in Hebei Province in North China were analyzed in this study. Results showed that: (1) the concentration of IVOCs measured was within the range of 11.3~85.1 μg·cm−3 during haze episodes, with normal alkanes (n-alkanes), polycyclic aromatic hydrocarbons (PAHs), branched alkanes (b-alkanes), and the residue unresolved complex mixture (R-UCM) accounting for 8.6 ± 2.3%, 6.8 ± 2.2%, 24.1 ± 3.8%, and 60.5 ± 6.5% of IVOCs, respectively. NC12-nC15 in n-alkanes, naphthalene and its alkyl substitutes in PAHs, b-alkanes in B12–B16 bins, and R-UCM in B12–B16 bins are the main components, accounting for 87.0 ± 0.2%, 87.6 ± 2.9%, 85.9 ± 5.4%, 74.0 ± 8.3%, respectively. (2) Based on the component characteristics of IVOCs and the ratios of n-alkanes/b-alkanes in emission sources and the hourly variation of IVOCs during haze episodes, coal combustion (CC), biomass burning (BB), gasoline vehicles (GV), and diesel vehicles (DV)were identified as important emission sources of IVOCs in Hebei Province. (3) During haze episodes, temporal variation of the estimated SOA production based on different methods (such as IVOCs concentration, OC/ECmin tracer, and the PMF model) were similar; however, the absolute values were different. This difference may be due to the transformation of IVOCs to SOA affected by various factors such as SOA production from different IVOC components, meteorological conditions, atmospheric oxidation, etc.

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 PM2.5 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.

In recent years, the Indian capital city of Delhi has been impacted by very high levels of air pollution, especially during winters. Comprehensive knowledge of the composition and sources of the organic aerosol (OA), which constitutes a substantial fraction of total particulate mass (PM) in Delhi, is central to formulating effective public health policies. Previous source apportionment studies in Delhi identified key sources of primary OA (POA) and showed that secondary OA (SOA) played a major role, but were unable to resolve specific SOA sources. We address the latter through the first field deployment of an extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) in Delhi, together with a high-resolution aerosol mass spectrometer (AMS). Measurements were conducted during the winter of 2018/2019, and positive matrix factorization (PMF) was used separately on AMS and EESI-TOF datasets to apportion the sources of OA. AMS PMF analysis yielded three primary and two secondary factors which were attributed to hydrocarbon-like OA (HOA), biomass burning OA (BBOA-1 and 2), more oxidized oxygenated OA (MO-OOA), and less oxidized oxygenated OA (LO-OOA). On average, 40 % of the total OA mass was apportioned to the secondary factors. The SOA contribution to total OA mass varied greatly between daytime (76.8 %, 10:00–16:00 local time (LT)) and nighttime (31.0 %, 21:00–04:00 local time). The higher chemical resolution of EESI-TOF data allowed identification of individual SOA sources. The EESI-TOF PMF analysis in total yielded six factors, two of which were primary factors (primary biomass burning and cooking-related OA). The remaining four factors were predominantly of secondary origin: aromatic SOA, biogenic SOA, aged biomass burning SOA, and mixed urban SOA. Due to the uncertainties in the EESI-TOF ion sensitivities, mass concentrations of EESI-TOF SOA dominated factors were related to the total AMS SOA (i.e., MO-OOA + LO-OOA) by multi-linear regression (MLR). Aromatic SOA was the major SOA component during the day-time, with 55.2 % contribution to total SOA mass (42.4 % contribution to total OA). Its contribution to total SOA, however, decreased to 25.4 % (7.9 % of total OA) during night-time. This factor was attributed to the oxidation of light aromatic compounds emitted mostly from traffic. Biogenic SOA accounted for 18.4 % of total SOA mass (14.2 % of total OA) during day-time and 36.1 % of total SOA mass (11.2 % of total OA) during night-time. Aged biomass burning and mixed urban SOA accounted for 15.2 % and 11.0 % of total SOA mass ( 11.7 % and 8.5 % of total OA mass) during day-time respectively and 15.4 % and 22.9 % of total SOA mass (4.8 % and 7.1 % of total OA mass) during night-time, respectively. A simple dilution/partitioning model was applied on all EESI-TOF factors to estimate the fraction of observed day-time concentrations resulting from local photochemical production (SOA) or emissions (POA). Aromatic SOA, aged biomass burning, and mixed urban SOA were all found to be dominated by local photochemical production, likely from the oxidation of locally emitted VOCs. In contrast, biogenic SOA was related to the oxidation of diffuse regional emissions of isoprene and monoterpenes. The findings of this study show that in Delhi, the night-time high concentrations are caused by POA emissions led by traffic and biomass burning, and the daytime OA is dominated by SOA, with aromatic SOA accounting for the largest fraction. Because aromatic SOA is possibly more toxic than biogenic SOA and primary OA, its dominance during the day-time suggests an increased OA toxicity and health-related consequences for the general public.

In recent years, the Indian capital city of Delhi has been impacted by very high levels of air pollution, especially during winters. Comprehensive knowledge of the composition and sources of the organic aerosol (OA), which constitutes a substantial fraction of total particulate mass (PM) in Delhi, is central to formulating effective public health policies. Previous source apportionment studies in Delhi identified key sources of primary OA (POA) and showed that secondary OA (SOA) played a major role, but were unable to resolve specific SOA sources. We address the latter through the first field deployment of an extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) in Delhi, together with a high-resolution aerosol mass spectrometer (AMS). Measurements were conducted during the winter of 2018/2019, and positive matrix factorization (PMF) was used separately on AMS and EESI-TOF datasets to apportion the sources of OA. AMS PMF analysis yielded three primary and two secondary factors which were attributed to hydrocarbon-like OA (HOA), biomass burning OA (BBOA-1 and 2), more oxidized oxygenated OA (MO-OOA), and less oxidized oxygenated OA (LO-OOA). On average, 40 % of the total OA mass was apportioned to the secondary factors. The SOA contribution to total OA mass varied greatly between daytime (76.8 %, 10:00–16:00 local time (LT)) and nighttime (31.0 %, 21:00–04:00 local time). The higher chemical resolution of EESI-TOF data allowed identification of individual SOA sources. The EESI-TOF PMF analysis in total yielded six factors, two of which were primary factors (primary biomass burning and cooking-related OA). The remaining four factors were predominantly of secondary origin: aromatic SOA, biogenic SOA, aged biomass burning SOA, and mixed urban SOA. Due to the uncertainties in the EESI-TOF ion sensitivities, mass concentrations of EESI-TOF SOA dominated factors were related to the total AMS SOA (i.e., MO-OOA + LO-OOA) by multi-linear regression (MLR). Aromatic SOA was the major SOA component during the day-time, with 55.2 % contribution to total SOA mass (42.4 % contribution to total OA). Its contribution to total SOA, however, decreased to 25.4 % (7.9 % of total OA) during night-time. This factor was attributed to the oxidation of light aromatic compounds emitted mostly from traffic. Biogenic SOA accounted for 18.4 % of total SOA mass (14.2 % of total OA) during day-time and 36.1 % of total SOA mass (11.2 % of total OA) during night-time. Aged biomass burning and mixed urban SOA accounted for 15.2 % and 11.0 % of total SOA mass ( 11.7 % and 8.5 % of total OA mass) during day-time respectively and 15.4 % and 22.9 % of total SOA mass (4.8 % and 7.1 % of total OA mass) during night-time, respectively. A simple dilution/partitioning model was applied on all EESI-TOF factors to estimate the fraction of observed day-time concentrations resulting from local photochemical production (SOA) or emissions (POA). Aromatic SOA, aged biomass burning, and mixed urban SOA were all found to be dominated by local photochemical production, likely from the oxidation of locally emitted VOCs. In contrast, biogenic SOA was related to the oxidation of diffuse regional emissions of isoprene and monoterpenes. The findings of this study show that in Delhi, the night-time high concentrations are caused by POA emissions led by traffic and biomass burning, and the daytime OA is dominated by SOA, with aromatic SOA accounting for the largest fraction. Because aromatic SOA is possibly more toxic than biogenic SOA and primary OA, its dominance during the day-time suggests an increased OA toxicity and health-related consequences for the general public.