Sources Of Fine Particulate Matter Research Articles

Integrating spatially distributed data into Positive Matrix Factorization to identify the hotspots of local emission sources

The receptor model of Positive Matrix Factorization (PMF) is widely used to identify the air pollution sources of fine particulate matter (PM2.5). However, using data collected at a single site in PMF analysis limits its ability to characterize the source emission regions. To recognize the hotspots of emission sources, this study incorporated spatially distributed PM2.5 mass concentrations into PMF modeling to estimate the spatial distribution of source-specific PM2.5. A local emission source of road dust/civil construction was retrieved and selected for evaluation of the characterized hotspot regions. The spatial distribution of the corresponding source-specific PM2.5 contributions in the study area was compared with the land use features. The positive correlations (coefficients ≥0.40) were acquired between source-specific PM2.5 and land use characteristics, such as major road length and the number of construction sites. A leave-one-out cross-validation R2 of 0.48 was achieved using the land use regression model. These findings demonstrated the effectiveness of the proposed approach in capturing spatial variations of local emission sources. Future studies are suggested to expand the study area to further assess the extent of application of the PMF spatialization results.

Quantifying vehicle restriction related PM2.5 reduction using field observations in an isolated urban basin

Vehicle (related particulate matter) emissions, including primary vehicle (related particulate matter) emissions, secondary nitrate, and road dust, have become an important source of fine particulate matter (PM2.5) in many cities across the world. The relationship between vehicle emissions and PM2.5 during vehicle restrictions has not yet been revealed using field observational data. To address this issue, a three-month field campaign on physical and chemical characteristics of PM2.5 at hourly resolution was conducted in Lanzhou, an urban basin with a semi-arid climate. The Lanzhou municipal government implemented more strict vehicle restriction measure during the latter part of field campaign period. The concentration of nitrogen oxides (NO x ) and PM2.5 decreased by 15.6% and 10.6%, respectively during the strict vehicle restriction period. The daily traffic fluxes decreased by 11.8% due to the vehicle restriction measure. The vehicle emission reduction led to a decrease of 2.43 μg·m−3 in PM2.5, including the decrease of primary vehicle emissions, secondary nitrate, and road dust. The contribution of vehicle emissions to PM2.5 decreased by 9.0% based on the results derived from a positive matrix factorization model. The sources other than vehicle emissions increased by 0.2 μg·m−3. Combining all evidence from the observations, the reduction of vehicle emissions is almost equal to the observed reduction in PM2.5. A further extrapolation that 9.0% reduction in vehicle emissions led to the observed reduction in PM2.5 (2.32 μg·m−3). This study clearly quantifies the vehicle restriction related PM2.5 reduction using field observations. The results provide scientific support for the implementation of effective vehicle emission reduction measures.

Short-term source apportionment of fine particulate matter with time-dependent profiles using SoFi Pro: exploring the reliability of rolling positive matrix factorization (PMF) applied to bihourly molecular and elemental tracer data

Abstract. Positive matrix factorization (PMF) has been widely used to apportion the sources of fine particulate matter (PM2.5) by utilizing PM chemical speciation data measured at the receptor site(s). Traditional PMF, which typically relies on long-term observational datasets of daily or lower time resolution to meet the required sample size, has its reliability undermined by changes in source profiles; thus, it is inherently ill-suited for apportioning sporadic sources or ephemeral pollution events. In this study, we explored short-term source apportionment of PM2.5 using a set of bihourly chemical speciation data over a period of 37 d in the winter of 2019–2020. PMF run with campaign-wide data as input (PMFref) was initially conducted to obtain reference profiles for the primary source factors. Subsequently, short-term PMF analysis was performed using the Source Finder Professional (SoFi Pro). The analysis sets a window length of 18 d and constrained the primary source profiles using the a-value approach embedded in SoFi Pro software. Rolling PMF was then conducted with a fixed window length of 18 d and a step of 1 d using the remaining dataset. By applying the a-value constraints to the primary sources, the rolling PMF effectively reproduced the individual primary sources, as evidenced by the slope values close to unity (i.e., 0.9–1.0). However, the estimation for the firework emission factor in the rolling PMF was lower compared with PMFref (slope: 0.8). These results suggest the unique advantage of short-term PMF analysis in accurately apportioning sporadic sources. Although the total secondary sources were well modeled (slope: 1.0), larger biases were observed for individual secondary sources. The variation in source profiles indicated higher variabilities for the secondary sources, with average relative differences ranging from 42 % to 173 %, while the primary source profiles exhibited much smaller variabilities (relative differences of 8 %–26 %). This study suggests that short-term PMF analysis with the a-value constraints in SoFi Pro can be utilized to apportion primary sources accurately, while future efforts are needed to improve the prediction of individual secondary sources. Additionally, future rapid source apportionment analysis can benefit from utilizing a library of source profiles derived from existing measurement data, thereby significantly reducing the time lag associated with receptor modeling source apportionment techniques.

Source apportionment of ambient PM2.5 in Ho Chi Minh City, Vietnam

The emission sources of fine particulate matter (PM2.5) have not yet been fully identified in Ho Chi Minh City (HCMC), Vietnam, presenting difficulties to authorities in controlling air pollution efficiently. To address this issue, this study explores the source apportionment of PM2.5 by the positive matrix factorization (PMF) model and identifies potential regional sources through the weighted concentration-weighted trajectory (WCWT) model based on the field observation data of PM2.5 in HCMC. 24-h PM2.5 samples were collected in central HCMC for a year (September 2019–August 2020). Herein, inductively coupled plasma mass spectroscopy was used to analyze trace elements, in addition to identifying PM2.5 mass and other chemical species, such as water-soluble ions and carbonaceous species, reported in our former study. The PMF results showed that PM2.5 in HCMC was dominated by anthropogenic-rich sources comprising biomass burning, coal combustion, transportation, and crustal origins (36.4% of PM2.5 mass), followed by secondary ammonium sulfate (18.4%), sea salt (13.7%), road dust (9.6%), and coal and crude oil combustion (9.4%). WCWT results suggested that the geological sources of PM2.5 were mainly from local areas and scattered to the northeast/southwest of HCMC. In addition, the long-range transport of PM2.5 from surrounding countries was revealed during the assembly restriction and lockdown period in 2020.Graphical

Particulate Pb emission factors from wildland fires in the United States

Wildland fires, which includes both wild and prescribed fires, and agricultural fires in sum are one of the largest sources of fine particulate matter (PM2.5) emissions to the atmosphere in the United States (US). Although wildland fire PM2.5 emissions are primarily composed of carbonaceous material, many other elements including trace metals are emitted at very low levels. Lead (Pb) is a US Environmental Protection Agency (EPA) criteria pollutant that is ubiquitous in the environment at very low concentrations including in biomass that can burn and emit Pb into the atmosphere. Although fires may emit Pb at very low concentrations, they can be a source of sizeable Pb emissions to the atmosphere because of the large quantity of PM2.5 emitted from fires. In this work, we measure Pb concentrations in unburned biomass, ash/residues, and particulate matter <2.5 μm (PM2.5) emitted from wildland fires using in-field measurements near prescribed fires and in laboratory simulations. Emission factors were calculated for multiple biomass types, representative of different regions of the US including grasslands in Oregon and Kansas; forest litter from Oregon, Montana, Minnesota, and North Carolina; and peat cores from Minnesota. Most of the biomass Pb remains in the ash/residues. The small percentage (<10%) that is emitted in PM2.5 is dependent on the biomass Pb concentration. The emissions factors measured here are several orders of magnitude lower than some reported in the literature, but the studies exhibited a wide range of values, which may be due to large uncertainties in the measurement method rather than differences in Pb emissions. Wildland fires are expected to increase in size and frequency in future years and these new emission factors can be used to improve the accuracy of Pb emissions estimates and better constrain our understanding of Pb emissions to the atmosphere.

Machine learning reveals the effects of drivers on PM2.5 and CO2 based on ensemble source apportionment method

Air pollution and climate change are currently common global concern issues. Understanding the main pollution sources of fine particulate matter (PM2.5) and carbon dioxide (CO2), and the drivers that accelerate their concentrations can provide novel insights into the co-benefits of air-quality improvement and climate change mitigation. Hence, in this study, we conducted source apportionment of PM2.5 and CO2, and obtained impacts of common sources on PM2.5-CO2 using an ensemble source apportionment method. Then, machine learning (ML) methods were utilized to study the effects of different drivers (including main pollution sources and meteorological factors) on PM2.5 and CO2. The results demonstrate that coal combustion (40%), vehicle exhaust (37%), and industrial emissions (23%) were the main common sources of PM2.5 and CO2. Controlling the combustion of fossil fuels and long-term vehicle electrification still hold significant importance for reducing pollutions and carbon emissions. The results of data-driven ML models show that temperature had the highest effect among meteorological factors, reflecting the seasonal changes of CO2 and PM2.5. The study provides a feasible framework for exploring co-benefits of clean air policies on greenhouse gas emissions based on online observation dataset.

Exposure to cooking fumes is associated with perturbations in nasal microbiota composition: A pilot study

Air pollution is one of the leading causes of overall mortality globally. Cooking emissions are a major source of fine particulate matter (PM2.5). However, studies on their potential perturbations on the nasal microbiota as well as their association with respiratory health are lacking. This pilot study aims to assess the environmental air quality among occupational cooks and its associations with nasal microbiota and respiratory symptoms. A total of 20 cooks (exposed) and 20 unexposed controls (mainly office workers), were recruited in Singapore from 2019 to 2021. Information on sociodemographic factors, cooking methods, and self-reported respiratory symptoms were collected using a questionnaire. Personal PM2.5 concentrations and reactive oxygen species (ROS) levels were measured using portable sensors and filter samplers. DNA was extracted from nasal swabs and sequenced using 16s sequencing. Alpha-diversity and beta-diversity were calculated, and between-group variation analysis of species was performed. Multivariable logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations between exposure groups and self-reported respiratory symptoms. Higher daily mean PM2.5 (P = 2 × 10−7) and environmental ROS exposure (P = 3.25 × 10−7) were observed in the exposed group. Alpha diversity of the nasal microbiota between the two groups was not significantly different. However, beta diversity was significantly different (unweighted UniFrac P = 1.11 × 10–5, weighted UniFrac P = 5.42 × 10−6) between the two exposure groups. In addition, certain taxa of bacteria were slightly more abundant in the exposed group compared to unexposed controls. There were no significant associations between the exposure groups and self-reported respiratory symptoms. In summary, the exposed group had higher PM2.5 and ROS exposure levels and altered nasal microbiotas as compared to unexposed controls, though further studies are required to replicate these findings in a larger population.

Willingness to Pay for Renewably-Sourced Home Heating in the Fairbanks North Star Borough

Space heating is a necessity in Alaska; however, the use of heating fuels carries both economic and environmental costs. In the Fairbanks North Star Borough (FNSB), Alaska, most households utilize heating fuel oil as a primary source for home heating and firewood as a secondary source. In the FNSB, wood-burning devices are the principal source of fine particulate matter with a size of 2.5 microns or less, (PM2.5), but firewood is less expensive when compared to heating fuel oil. The FNSB has been designated as a nonattainment area for PM2.5, which has been linked to negative cardiopulmonary impacts and other adverse health consequences. Electric thermal storage heaters (ETSH) could help solve the PM2.5 problem by displacing firewood used for residential space heating. We use dichotomous choice contingent valuation (DC-CV) to estimate willingness to pay (WTP) for an ETSH program which would allow FNSB residents to offset 100 gallons of heating fuel oil annually. Certainty correction is used to control for the presence of hypothetical bias. We find median WTP is USD 33.98 without certainty correction and USD 9.75 with certainty correction. Our results indicate that implementation of a special ETSH electricity rate based on the WTP estimate may lead to broader adoption of ETSH for space heating, which could improve air quality, reduce fuel poverty, and reduce the carbon footprint of residential space heating.

Characteristics of particulate-bound n-alkanes indicating sources of PM2.5 in Beijing, China

Abstract. The characteristics of n-alkanes and the contributions of various sources of fine particulate matter (PM2.5) in the atmosphere in Beijing were investigated. PM2.5 samples were collected at Minzu University of China between November 2020 and October 2021, and n-alkanes in the samples were analyzed by gas chromatography mass spectrometry. A positive matrix factorization analysis model and source indices (the main carbon peaks, carbon preference indices, and plant wax contribution ratios) were used to identify the sources of n-alkanes, to determine the contributions of different sources, and to explain the differences. The n-alkane concentrations were 4.51–153 ng m−3 (mean 32.7 ng m−3), and the particulate-bound n-alkane and PM2.5 concentrations varied in parallel. There were marked seasonal and diurnal differences in the n-alkane concentrations (p&lt;0.01). The n-alkane concentrations in the different seasons decreased in the order of winter &gt; spring &gt; summer &gt; fall. The mean concentration of each homolog was higher at night than in the day in all seasons. Particulate-bound n-alkanes were supplied by common anthropogenic and biogenic sources, and fossil fuel combustion was the dominant contributor. The positive matrix factorization model results indicated five sources of n-alkanes in PM2.5, which were coal combustion, diesel vehicle emissions, gasoline vehicle emissions, terrestrial plant release, and mixed sources. Vehicle emissions were the main sources of n-alkanes, contributing 57.6 %. The sources of PM2.5 can be indicated by n-alkanes (i.e., using n-alkanes as organic tracers). Vehicle exhausts strongly affect PM2.5 pollution. Controlling vehicle exhaust emissions is key to controlling n-alkanes and PM2.5 pollution in Beijing.

Sources of PM2.5-Associated Health Risks in Europe and Corresponding Emission-Induced Changes During 2005-2015.

We present a newly developed approach to characterize the sources of fine particulate matter (PM2.5)-related premature deaths in Europe using the chemical transport model GEOS-Chem and its adjoint. The contributions of emissions from each individual country, species, and sector are quantified and mapped out at km scale. In 2015, total PM2.5-related premature death is estimated to be 449,813 (257,846-722,138) in Europe, 59.0% of which were contributed by domestic anthropogenic emissions. The anthropogenic emissions of nitrogen oxides, ammonia, and organic carbon contributed most to the PM2.5-related health damages, making up 29.6%, 23.2%, and 16.8%, respectively of all domestic anthropogenic contributions. Residential, agricultural, and ground transport emissions are calculated to be the largest three sectoral sources of PM2.5-related health risks, accounting for 23.5%, 23.0%, and 19.4%, respectively, of total anthropogenic contributions within Europe. After excluding the influence of extra-regional sources, we find eastern European countries suffered from more premature deaths than their emissions caused; in contrast, the emissions from some central and western European regions contributed premature deaths exceeding three times the number of deaths that occurred locally. During 2005-2015, the first decade of PM2.5 regulation in Europe, emission controls reduced PM2.5-related health damages in nearly all European countries, resulting in 63,538 (46,092-91,082) fewer PM2.5-related premature deaths. However, our calculation suggests that efforts to reduce air pollution from key sectors in some countries can be offset by the lag in control of emissions in others. International cooperation is therefore vitally important for tackling air pollution and reducing corresponding detrimental effects on public health.

Diagnosing domestic and transboundary sources of fine particulate matter (PM2.5) in UK cities using GEOS-Chem

The UK is set to impose a stricter ambient annual mean fine particulate matter (PM2.5) standard than was first adopted fourteen years ago. This necessitates strengthened knowledge of the magnitude and sources that influence urban PM2.5 in UK cities to ensure compliance and improve public health. Here, we use a regional-scale chemical transport model (GEOS-Chem), validated with national ground-based observations, to quantify the influence of specific sources within and transported to the mid-sized UK city Leicester. Of the sources targeted, we find that agricultural emissions of ammonia (NH3) make the largest contribution (3.7 μg m−3 or 38 % of PM2.5) to annual mean PM2.5 in Leicester. Another important contributor is long-range transport of pollution from continental Europe accounting for 1.8 μg m−3 or 19 % of total annual mean PM2.5. City sources are a much smaller portion (0.2 μg m−3; 2 %). We also apply GEOS-Chem to the much larger cities Birmingham and London to find that agricultural emissions of NH3 have a greater influence than city sources for Birmingham (32 % agriculture, 19 % city) and London (25 % agriculture, 13 % city). The portion from continental Europe is 16 % for Birmingham and 28 % for London. Action plans aimed at national agricultural sources of NH3 and strengthened supranational agreements would be most effective at alleviating PM2.5 in most UK cities.

Predicting of Daily PM2.5 Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China.

Anthropogenic sources of fine particulate matter (PM2.5) threaten ecosystem security, human health and sustainable development. The accuracy prediction of daily PM2.5 concentration can give important information for people to reduce their exposure. Artificial neural networks (ANNs) and wavelet-ANNs (WANNs) are used to predict daily PM2.5 concentration in Shanghai. The PM2.5 concentration in Shanghai from 2014 to 2020 decreased by 39.3%. The serious COVID-19 epidemic had an unprecedented effect on PM2.5 concentration in Shanghai. The PM2.5 concentration during the lockdown in 2020 of Shanghai is significantly reduced compared to the period before the lockdown. First, the correlation analysis is utilized to identify the associations between PM2.5 and meteorological elements in Shanghai. Second, by estimating twelve training algorithms and twenty-one network structures for these models, the results show that the optimal input elements for daily PM2.5 concentration predicting models were the PM2.5 from the 3 previous days and fourteen meteorological elements. Finally, the activation function (tansig-purelin) for ANNs and WANNs in Shanghai is better than others in the training, validation and forecasting stages. Considering the correlation coefficients (R) between the PM2.5 in the next day and the input influence factors, the PM2.5 showed the closest relation with the PM2.5 1 day lag and closer relationships with minimum atmospheric temperature, maximum atmospheric pressure, maximum atmospheric temperature, and PM2.5 2 days lag. When Bayesian regularization (trainbr) was used to train, the ANN and WANN models precisely simulated the daily PM2.5 concentration in Shanghai during the training, calibration and predicting stages. It is emphasized that the WANN1 model obtained optimal predicting results in terms of R (0.9316). These results prove that WANNs are adept in daily PM2.5 concentration prediction because they can identify relationships between the input and output factors. Therefore, our research can offer a theoretical basis for air pollution control.

Numerical simulations of the effects of green infrastructure on PM2.5 dispersion in an urban park in Bangkok, Thailand

Traffic emission has been identified as one of the dominant sources of fine particulate matter (PM2.5) in Thailand, and urban green spaces have the capacity to mitigate air pollution. Taking Bangkok as the study area, one of the most polluted cities in Thailand, this study investigated the effect of vegetation on PM2.5 concentration at three different sites with different vegetation characteristics in Chatuchak Park, an urban park located in Bangkok. Sensors were installed at the park to measure PM2.5 and metrological parameters at the roadside and different distances inside the park away from the road, and the ENVI-met model was run to simulate PM2.5 concentration in the three study sites. The result shows that PM2.5 concentration behind the vegetation barrier was reduced 34% on average compared to the concentration next to the road at the three sites. The effect of vegetation on meteorological factors was clearly seen near the park border with a hedgerow grown along the border. The order of influence of meteorological factors on PM2.5 concentration was relative humidity > potential temperature > wind speed > wind direction. Two scenarios including changes in weather patterns and types of vegetation that affect PM2.5 concentration were studied. Changing in the wind direction from oblique to perpendicular to the park had no significant effect on PM2.5 concentration as long as there is a dense hedgerow along the park border. Comparing to the current vegetation, sparse vegetation with less leaf area density and higher crown base heights had lower impact to mitigate PM2.5 concentration in the park. Our study provides information on vegetation and landscape strategies which can provide good air quality in the urban parks for better park design in the future.

Estimation of exposure and premature mortality from near-roadway fine particulate matter concentrations emitted by heavy-duty diesel trucks in Beijing

Traffic exhaust is a main source of fine particulate matter (PM2.5) in cities. Heavy-duty diesel trucks (HDDTs), the primary mode of freight transport, contribute significantly to PM2.5, posing a great threat to public health. However, existing research based on dispersion models to simulate pollutant concentrations lacks high-spatiotemporal-resolution emission inventories of HDDTs as input data, and the public health effects of such emissions in different populations have not been thoroughly assessed. To fill this gap, we focused on Beijing as the research area and developed a high-resolution PM2.5 emission inventory for HDDTs based on Global Navigation Satellite System-equipped vehicle trajectory data. We then simulated the fine-scale spatial distribution of diesel-related PM2.5 and assessed the population exposure by integrating the dispersion model and population distributions. Further, we quantified the mortality attributable to noncommunicable diseases (NCDs) plus lower respiratory infections (LRIs) related to PM2.5 emissions from HDDTs. Results showed that 3.3% of Beijing people lived in areas with high PM2.5 HDDT emissions, which were near intercity highways. Furthermore, the estimated number of NCD + LRI annual premature deaths attributed to PM2.5 HDDT emissions in Beijing was 339 (95% CI: 276–401). The NCD + LRI mortality increased with age, and deaths were more frequent in males than females. Our results aid the identification of HDDT PM2.5 emission exposure hotspots for the formulation of effective mitigation measures and provide important insights into the adverse health impacts of HDDT emissions.

Hourly population exposure index for PM2.5 in urban street canyons

Vehicle emissions are among the primary sources of fine particulate matter (PM2.5). Assessing population exposure levels to PM2.5 from traffic emissions in street canyons is imperative. Accordingly, this study developed an hourly population exposure index (HPEI) by combining PM2.5 concentration distributions determined using computational fluid dynamics–based simulations, outdoor/indoor (O/I) concentration ratios of PM2.5 determined experimentally, and population distributions determined through an investigation. The results indicated that strength of vehicle emission sources and meteorological conditions affected the PM2.5 concentration distributions in the examined street canyon. The mean PM2.5 concentration in the middle of the street was higher than the concentrations at other positions, and PM2.5 concentrations on the leeward sides of buildings were higher than those on the windward sides. Moreover, three to five peaks of PM2.5 concentrations were observed at different measurement positions on the street and were attributed to the formation of vortex flows. The O/I concentration ratios of sulfur in PM2.5 in home, office, school, bus, and car microenvironments were 0.47, 0.50, 0.51, 0.77, and 0.61, respectively. The HPEI most closely reflected the population distribution at 7:00; however, it was derived using the PM2.5 concentration distributions and population distributions observed at 10:00. These results may help researchers understand the population exposure distribution and improve urban environmental management strategies.

Spatiotemporal variations and sources of PM2.5 in the Central Plains Urban Agglomeration, China.

The Central Plains Urban Agglomeration (CPUA) is the largest region in central China and suffers from serious air pollution. To reveal the spatiotemporal variations and the sources of fine particulate matter (PM2.5, with an aerodynamic diameter of smaller than 2.5 μm) concentrations of CPUA, multiple and transdisciplinary methods were used to analyse the collected millions of PM2.5 concentration data. The results showed that during 2017 ~ 2020, the yearly mean concentrations of PM2.5 for CPUA were 68.3, 61.5, 58.7, and 51.5 μg/m3, respectively. The empirical orthogonal function (EOF) analysis suggested that high PM2.5 pollution mainly occurred in winter (100.8 μg/m3, 4-year average). The diurnal change in PM2.5 concentrations varied slightly over the season. The centroid of the PM2.5 concentration moved towards the west over time. The spatial autocorrelation analysis indicated that PM2.5 concentrations exhibited a positive spatial autocorrelation in CPUA. The most polluted cities distributed in the northern CPUA (Handan was the centre) formed a high-high agglomeration, and the cities located in the southern CPUA (Xinyang was the centre) formed a low-low agglomeration. The backward trajectory model and potential source contribution function were employed to discuss the regional transportation of PM2.5. The results demonstrated that internal-region and cross-regional transport of anthropogenic emissions were all important to PM2.5 pollution of CPUA. Our study suggests that joint efforts across cities and regions are necessary.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11869-022-01178-z.

Development of methods for citizen scientist mapping of residential woodsmoke in small communities

BackgroundResidential wood burning is a major source of fine particulate matter (PM2.5) during winter and a leading contributor to air pollution. Exposure to woodsmoke PM2.5 is associated with many health effects, so it is important to characterize the magnitude and spatial variability in exposures. However, high infrastructure and maintenance costs of regulatory monitoring stations limit their spatial resolution and make monitoring infeasible for many small communities where woodsmoke may be prevalent. MethodsMobile monitoring was conducted with a nephelometer and multi-wavelength aethalometer, capable of identifying woodsmoke PM2.5, to capture spatially resolved data. This Combined Aethalometer and Nephelometer for Assessment of Woodsmoke (CANAW) method was evaluated in three pairs of communities in British Columbia, Canada. Measurements were also taken at fixed-site monitoring stations. Light scattering measured by a nephelometer (Bsp) was compared with gravimetric filter-based and beta-attenuation measures of PM2.5. The difference in absorbance of 370 nm and 880 nm wavelengths as measured by an aethalometer (delta C), was compared with the chemical woodsmoke tracer levoglucosan. ResultsFixed site measurements of Bsp and delta C were comparable with established methods of monitoring PM2.5 and woodsmoke, respectively. Correlations in each tested relationship across all locations were high (r ≥ 0.93 in all cases). Mobile monitoring captured high spatial variation in woodsmoke PM2.5 and maps of average concentrations during monitoring were created to identify woodsmoke hotspots. ConclusionFollowing the successful implementation of the mobile CANAW method, training materials were created and tested with lay volunteers along with an online mapping application. Volunteers were able to effectively operate the equipment, collect valuable data on woodsmoke concentrations, and map spatial patterns across their communities using the application. The CANAW method is a valuable option for advancing cost-effective data collection for residential woodsmoke in otherwise unmonitored communities, and to add spatial context to existing monitoring networks.

Fine particulate matter pollution characteristics and source apportionment of Changchun atmosphere.

In order to study the pollution characteristics and main sources of fine particulate matter in the atmosphere of the city of Changchun, PM2.5 samples were collected during the four seasons in 2014, and representative months for each season are January, April, July, and October. Sample collection was carried out on 10 auto-monitoring stations in Changchun, and PM2.5 mass concentration and its chemical components (including inorganic elements, organic carbon, elemental carbon, and water-soluble ions) were measured. The results show that the annual average mass concentration of PM2.5 in Changchun in 2014 was about 66.77μg/m3. Organic matter was the highest component in PM2.5, followed by secondary inorganic ions (SNA), mineral dust (MIN), elemental carbon (EC), and trace elements (TE). Positive matrix factorization (PMF) results gave seven factors, namely, industrial, biomass and coal burning, industrial and soil dust, motor vehicle, soil and secondary ion, light industrial, and hybrid automotive and industrial sources in PM2.5, with contributing values of 18.9%, 24.2%, 5.7%, 23.0%, 11.5%, 13.0%, and 3.6%, respectively.

Measurement and Analysis of PM10 and PM2.5 from Chimneys of Coal-fired Power Plants Using a Light Scattering Method

Aerial pollutants emitted from the stacks of coal-fired power plants are considered a major source of fine particulate matter released into the atmosphere. To manage such fine particles in the stacks themselves, it is necessary to know the concentration of fine particles emitted in real time; the current system to do so is tedious. In this study, a system for measuring fine particles emitted from the stacks of power plants in real-time was developed, and measurements were performed on six coal-fired power plants. Through these measurements, the mass concentration distribution, according to particle sizes, could be determined. All six stacks showed bimodal distribution, and the count median diameters of each mode were 0.5 µm and 1.1 µm. Additionally, data were compared using the gravimetric measurement method; it was found that the relative accuracies for the measured PM10 amounts were within 20% and that the values obtained using the measuring instrument proposed in this study were reliable. Three power plants were continuously measured for one month, and by comparing PM10 concentration according to the amount of power generated, it was confirmed that PM10 discharged from the stack increased as an exponential function, depending on the amount of power generated.

Assessment of the Contribution of Local and Regional Biomass Burning on PM2.5 in New York/New Jersey Metropolitan Area

The sources of fine particulate matter (PM2.5, particles with diameter < 2.5 μm) in four monitoring sites in the New York/New Jersey metropolitan statistical area from 2007 to 2017 was apportioned by positive matrix factorization (PMF) of chemical speciation data. Biomass burning, secondary inorganic (i.e., ammonium sulfate and nitrate) and primary traffic exhausts were the predominant PM2.5 sources. The declining trends of PM2.5 mass in all four sites were very well correlated with decreasing secondary sulfate levels due to SO2 emission reductions by coal-fired power plants. The contributions of secondary nitrate, primary traffic exhausts and diesel particles did not change (or slightly increased) over time except for the Queens site, where statistically significant declines were computed. Biomass burning contributions increased in the Queens and Chester sites but declined in the Division Str and Elizabeth Lab sites, although significant interannual variability was observed. Wintertime biomass burning aerosols were most likely due to combustion of contemporary biomass for industrial and domestic heating, and it was linked to the intensity (average minimum temperature) and duration (number of freezing days) of cold weather. The annual summertime biomass burning contributions were correlated with the number and area burnt by lightning-ignited wildfires. These results indicate that PM2.5 sources in urban environments is changing from anthropogenic secondary sulfate and nitrate to carbonaceous aerosol from local anthropogenic and regional climate-driven biomass burning. This trend may counterbalance emissions controls on anthropogenic activities and modify the biological and toxicological responses and resultant health effects.