Twenty-five years after the first look at polycyclic aromatic compounds (PACs) in Canada, this article presents current knowledge on Canadian PAC emission sources. The analysis is based on national inventories (the National Pollutant Release Inventory (NPRI) and the Air Pollutant Emissions Inventory (APEI)), an analysis of Canadian forest fires, and several air quality model-ready emissions inventories. Nationally, forest fires continue to dominate PAC emissions in Canada, however there is uncertainty in these estimates. Though forest fire data show a steady average in the total annual area burned historically, an upward trend has developed recently. Non-industrial sources (home firewood burning, mobile sources) are estimated to be the second largest contributor (∼6-8 times lower than forest fires) and show moderate decreases (25%–65%) in the last decades. Industrial point sources (aluminum production, iron/steel manufacturing) are yet a smaller contributor and have seen considerable reductions (90% +) in recent decades. Fugitive emissions from other industrial sources (e.g. disposals by the non-conventional oil extraction and wastewater sectors, respectively) remain a gap in our understanding of total PAC emissions in Canada. Emerging concerns about previously unrecognized sources such as coal tar-sealed pavement run-off, climate change are discussed elsewhere in this special issue. Results affirm that observations at the annual/national scale are not always reflective of regional/local or finer temporal scales. When determining which sources contribute most to human and ecosystem exposure in various contexts, examination at regional and local scales is needed. There is uncertainty overall in emissions data stemming in part from various accuracy issues, limitations in the scope of the various inventories, and inventory gaps, among others.

Two hundred sixty-three fine particulate matter (PM₂.₅) samples were collected over fourteen months in Fresno and Bakersfield, California. Samples were analyzed for organic carbon (OC), elemental carbon (EC), water soluble organic carbon (WSOC), and 160 organic molecular markers. Chemical Mass Balance (CMB) and Positive Matrix Factorization (PMF) source apportionment models were applied to the results in order to understand monthly and seasonal source contributions to PM₂.₅ OC. Similar source categories were found from the results of the CMB and PMF models to PM₂.₅ OC across the sites. Six source categories with reasonably stable profiles, including biomass burning, mobile, food cooking, two different secondary organic aerosols (SOAs) (i.e., winter and summer), and forest fires were investigated. Both the CMB and the PMF models showed a strong seasonality in contributions of some sources, as well as dependence on wind transport for both sites. The overall relative source contributions to OC were 24% CMB wood smoke, 19% CMB mobile sources, 5% PMF food cooking, 2% CMB vegetative detritus, 17% PMF SOA summer, 22% PMF SOA winter, and 12% PMF forest fire. Back-trajectories using the Weather Research and Forecasting model combined with the FLEXible PARTicle dispersion model (WRF-FLEXPART) were used to further characterize wind transport. Clustering of the trajectories revealed dominant wind patterns associated with varying concentrations of the different source categories. The Comprehensive Air Quality Model with eXtensions (CAMx) was used to simulate aerosol transport from forest fires and thus confirm the impacts of individual fires, such as the Rough Fire, at the measurement sites.

High concentration particulate matter 2.5 released from forest fires, in addition to direct burns and asphyxia, PM₂.₅ is one of the main pollutants which threaten the safety of forest fire fighter. Therefore, to assess spatial distribution of PM₂.₅, a simulation study was conducted. Fuel beds with different moisture contents and loads were constructed. 144 times burning experiments were carried out under different wind speeds by using wind tunnel device. PM₂.₅ particles at different spatial points were collected and calculated. The results show that, in the two of three variables interaction between wind speed, fuel load, and, except fuel moisture content, wind speed and fuel load are positively correlated with the PM₂.₅ concentrations. From PM₂.₅ concentration which collected at each point in the horizontal and vertical directions, the overall trend is that PM₂.₅ concentration increases along the horizontal downwind direction (C and D higer than A and B) and the vertical upward direction (A and C higer than B and D) Based on BP neural network, the spatial distribution model of PM₂.₅ concentration with single hidden layer was established. The prediction accuracy of modeling samples and validation samples is balanced when hidden layer node is 5. This study will help to make reference for PM₂.₅ occupational exposure standards, forest fire smoke management and forest fire management in China.

The source for Lead (Pb) pollution in soils from the heavily industrialized area located along the coast of the Eastern Mediterranean, Haifa Bay, Northern Israel, is studied using the lead isotopic composition. The uniqueness of the studied data set is that it includes samples of soils, road-wash, and storm-dust sampled for nearly three decades (1988–2017). Road-wash sediments are similar in both elemental and Pb isotopic composition to soils sampled in the same year (2010), indicating re-suspension of local soil, as its origin. Soils sampled during and before 1993 show no evidence for Pb contamination (bulk soil values), although Pb as an additive was already in use. Furthermore, soil overturns hinder the possibility to trace changes in the Pb isotopic composition with time in soils of the same location. Soils sampled from 1995–8 to 2013 were significantly dominated by Post-1992 Pb additive, pointing to Pb’s peak as an additive. Soils Pb and Zn Enrichment factors for most samples are below 5, and their anthropogenic source is likely common. Forest fire enriched Pb and Zn in the soil, and their Pb isotope compositions reflect this enrichment. Lead from the Hod Assaf recycling plant detected up to some 2.5 km away, and although not analyzed in the current study, dioxin-like compounds possibly accompanied Pb.

Polycyclic aromatic hydrocarbons (PAHs) are formed by the incomplete combustion of fossil fuels and forest or biomass burning. PAHs undergo long-range atmospheric transport, as evidenced by in situ observations across the Arctic. However, monitored atmospheric concentrations of PAHs indicate that ambient PAH levels in the Arctic do not follow the declining trend of worldwide anthropogenic PAH emissions since the 2000s, suggesting missing sources of PAHs in the Arctic or other places across the Northern Hemisphere. To trace origins and causes for the increasing trend of PAHs in the Arctic, the present study reconstructed PAH emissions from forest fires in the northern boreal forest derived by combining forest carbon stocks and MODIS burned area. We examined the statistical relationships of forest biomass, MODIS burned area, emission factors, and combustion efficiency with different PAH congeners. These relationships were then employed to construct PAH emission inventories from forest biomass burning. We show that for some PAH congeners, for example, benzo[a]pyrene (BaP)—the forest-fire-induced air emissions are almost one order of magnitude higher than previous emission inventories in the Arctic. A global-scale atmospheric chemistry model, GEOS-Chem, was used to simulate air concentrations of BaP, a representative PAH congener primarily emitted from biomass burning, and to quantify the response of BaP to wildfires in the northern boreal forest. The results showed that BaP emissions from wildfires across the northern boreal forest region played a significant role in the contamination and interannual fluctuations of BaP in Arctic air. A source-tagging technique was applied in tracking the origins of BaP pollution from different northern boreal forest regions. We also show that the response of BaP pollution at different Arctic monitoring sites depends on the intensity of human activities.

Continuous measurements of Black Carbon (BC) aerosol mass concentrations were carried at Dehradun (30.33°N, 78.04°E, 700 m amsl), a semi-urban site in the foothills of north-westHimalayas, India during January 2011–December 2017. We reported both the BC seasonal variations as well as mass concentrations from fossil fuel combustion (BCff) and biomass burning (BCbb) sources. Annual mean BC exhibited a strong seasonal variability with maxima during winter (4.86 ± 0.78 μg m⁻³) followed by autumn (4.18 ± 0.54 μg m⁻³), spring (3.93 ± 0.75 μg m⁻³) and minima during summer (2.41 ± 0.66 μg m⁻³). Annual averaged BC mass concentrations were 3.85 ± 1.16 μg m⁻³ varying from 3.29 to 4.37 μg m⁻³ whereas BCff and BCbb ranged from 0.11 to 7.12 μg m⁻³ and 0.13–3.6 μg m⁻³. The percentage contributions from BCff and BCbb to total BC are 66% and 34% respectively, indicating relatively higher contribution from biomass burning as compared to other locations in India. This is explained using potential source contribution function (PSCF) and concentration weighted trajectories (CWT) analysis which reveals the potential sources of BC originating from the north-west and eastern parts of IGP and the western part of the Himalayas that are mostly crop residue burning and forest fire regions in India. The annual mean ARF at top-of-atmosphere (TOA), at surface (SUR), and within the atmosphere (ATM) were found to be −14.84 Wm⁻², −43.41 Wm⁻², and +28.57 Wm⁻² respectively. To understand the impact of columnar aerosol burden on ARF, the radiative forcing efficiency (ARFE) was estimated and averaged values were −31.81, −91.63 and 59.82 Wm⁻² τ⁻¹ for TOA, SUR and ATM respectively. The high ARFE within the atmosphere indicates the dominance of absorbing aerosol (BC and dust) over Northwest Himalayas.

Size-segregated ambient particles down to particles smaller than 0.1 μm (PM₀.₁) were collected during the year 2014–2015 using cascade air samplers with a PM₀.₁ stage, at two cities in Thailand, Bangkok and Chiang Mai. Their characteristics and seasonal behavior were evaluated based on the thermal/optical reflectance (IMPROVE_TOR) method. Diagnostic indices for their emission sources and the black carbon (BC) concentration were assessed using an aethalometer and related to the monthly emission inventory (EI) of particle-bound BC and organic carbon (OC) in order to investigate the contribution of agricultural activities and forest fires as well as agro-industries in Thailand. Monthly provincial EIs were evaluated based on the number of agricultural crops produced corresponding to field residue burning and the use of residues as fuel in agro-industries, and also on the number of hot spots from satellite images corresponding to the areas burned by forest fires. The ratio of char-EC/soot-EC describing the relative influence of biomass combustion to diesel emission was found to be in agreement with the EI of BC from biomass burning in the size range <1 μm. This was especially true for PM₀.₁, which usually tends to be indicative of diesel exhaust particles, and was shown to be very sensitive to the EI of biomass burning. In Chiang Mai, the northern part of Thailand, the forest fires located upwind of the monitoring site were found to be the largest contributor while the carbon behavior at the site in Bangkok was better accounted for by the EI of provinces in central Thailand including Bangkok and its surrounding provinces, where the burning of crop residues and the cultivation of sugarcane for sugar production are significant factors. This suggests that the influence of transportation of polluted air masses is important on a multi-provincial scale (100–200 km) in Thailand.

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

A study of 16 United States Environmental Protection Agency (USEPA) priority listed PAHs associated with particulate matter ≤ 10 μm (PM10) was conducted in Singapore during the period 29th May 2015 to 28th May 2016. The sampling period coincided with an extensive, regional smoke haze episode (5th September to 25th October) that occurred as a result of forest and peat fires in neighboring Indonesia. Throughout this study, 54 atmospheric PM10 samples were collected in 24 h periods using a high volume sampler (HVS) and quarts fiber filters (QFF) as the collection medium. Hysplit software for computing 3-D backward air mass trajectories, diagnostic ratio analysis and ring number distribution calculations were used to examine the sources of PAHs in the atmosphere in Singapore. Under normal conditions the total PAH concentrations were in a range from 0.68 ng m−3 to 3.07 ng m−3, while for the high haze period the results showed approximately double the concentrations with a maximum value of 5.97 ng m−3. Diagnostic ratio (DR) and principal component analysis (PCA) were conducted and indicated the contribution of the traffic as a dominant pyrogenic source of PAHs during normal periods, while results from the haze dataset showed relatively strong influence of smoke from peat and forest fires in Indonesia. Environmental and health risk from PAHs were assessed for both regular and hazy days.