Source Apportionment Of Particulate Matter Research Articles

Chemical characterization and source apportionment of atmospheric fine particulate matter (PM2.5) at an urban site in Astana, Kazakhstan

PM2.5 is a significant air quality concern in urban areas globally. PM2.5 in Astana exceeded the US EPA standard for annual mean PM2.5 EPA with an average PM2.5 mass concentration of 23.43 ± 11.50 μg/m3 and a maximum daily value of 63.8 μg/m3 in January (heating season). The lowest 3.7 μg/m3 was observed in September (non-heating season). However, source apportionment in Kazakhstan has not been done because of a lack of PM2.5 composition measurements. This study presents the first analysis of chemical composition of PM2.5 and source apportionments in Kazakhstan. It comprehensively examined Astana's PM2.5 chemical characteristics and source apportionment over ∼ two years (November 2019 to August 2021). Using elements, water-soluble inorganic ions, black carbon (BC), and estimated unmeasured mass (UMM), source apportionment was obtained using Positive Matrix Factorization (PMF) supported by conditional bivariate probability function (CBPF) analyses. Average PM2.5 contributions of the eight sources were ‘Spark Ignition’ (30.3%), ‘Coal Flyash’ (17.1%), ‘Secondary Nitrate’ (15.1%), ‘Primary Sulfate-Fuel Combustion’ (9.9%), ‘Secondary Sulfate-Coal Combustion’ (8.5%), ‘Soil/Road Dust’ (7.9%), ‘Diesel’ (7.1%), and ‘Local Power Plant(s)’ (4.2%). Local power plants burn high-ash coal and fuel oil year-round. The major contributions of heating/power plants, private residential heating systems, autonomous boilers, vehicles, asphalt pavement mixing facilities, and local construction activities. This study's source apportionment analysis provides critical insights for developing targeted air quality management strategies in Kazakhstan. Findings highlight the need for improved controls on vehicular emissions and heat/power generation sources and for the implementation of measures by local governments to effectively reduce PM2.5.

Source apportionment of PM2.5 and PM10 pollutants near an urban roadside site using positive matrix factorization

This paper presents results from a comprehensive study of source apportionment of particulate matter (PM) of size PM2.5 and PM10 near a busy highway in Sharjah, United Arab Emirates. Source apportionment was carried out using US Environmental Protection Agency (EPA) Positive Matrix Factorization model. Furthermore, backward trajectory analysis and Potential Source Contribution Function were used to assess air mass transport pathways and identify potential source regions, respectively. The results revealed six major sources for PM2.5, including traffic, sea salt, fugitive dust, secondary aerosols, heavy oil combustion and mineral dust. For PM10, four major sources were identified, including secondary aerosols, traffic, sea salt and mineral dust. Traffic emissions were found to be significant contributors to both PM2.5 and PM10 pollution, along with natural sources like sea salt and mineral dust. Backward trajectory analysis indicated the influence of different wind regimes on air mass transport, with contributions from regions like Arabian Gulf, Arabian Sea, Oman and Iran. The Conditional Bivariate Probability Function analysis further explained the impact of local traffic emissions and other sources on PM pollution under varying wind conditions.

Source apportionment of fine particulate matter in middle Indo-Gangetic Plain by coupled radiocarbon –molecular organic tracer method

Despite significant emissions of fine particulate matter (FPM) from the Indo-Gangetic Plain (IGP) that affect the climate and air quality in the region, the sources of these emissions are not adequately addressed. This research uses a combined radiocarbon-molecular organic tracer technique to investigate the degree of contamination, seasonal fluctuations, and contribution of FPM in the middle IGP (Patna), India. The findings indicated levoglucosan (L) as the single primary BB tracer chemical, ranging from 149 ng/m3 to 490 ng/m3 (median 282 ng/m3). Winter (median 462 ng/m3) showed a 2–3 times higher level of L than the monsoon season (median 180 ng/m3). A significant association of L with other organic tracers such as galactosan (G), mannosan (M), vallinic acid, syringic acid, p-hydroxybenzoic acid (pHA) and dehydroabietic acid (DHAA)(r = 0.53 to 0.89, p < 0.05), and moderate connection with Cl− (r = 0.21, p < 0.05), SO42− (r = 0.29, p < 0.05), and NO3- (r = 0.22, p < 0.05) indicated significant BB contribution. However, non-sea salt (nss-K+) was not related to L. Based on seven days of air mass back trajectories and MODIS active fire counts analysis, we conclude that OAs composition is not the local origin but is also impacted by long-range atmospheric transport from Pakistan/Afghanistan, followed by the Bay of Bengal and the Arabian Sea. Chemical analysis of organic tracers and positive matrix factorization (PMF) modeling study identified three unique sources, i.e., biomass burning, secondary aerosols formation, and mixed type (fossil fuels and construction dust) as the primary source of FPM in Patna, accounted for 46.1 %, 28.9 %, and 24.9 %, of total emissions, respectively. The radiocarbon (14 C) analysis of total carbon (TC) samples further supported this conclusion. The results of the 14 C study indicated that emissions from BB, such as wood and stubble, were responsible for 57% of the TC concentration.

Source Apportionment of Particulate Matter in a Metal Workshop.

Metal workshops are workplaces with the substantial production of particulate matter (PM) with high metal content, which poses a significant health risk to workers. The PM produced by different metal processing techniques differs considerably in its elemental composition and size distribution and therefore poses different health risks. In some previous studies, the pollution sources were isolated under controlled conditions, while, in this study, we present a valuable alternative to characterize the pollution sources that can be applied to real working environments. Fine PM was sampled in five units (partially specializing in different techniques) of the same workshop. A total of 53 samples were collected with a temporal resolution of 30 min and 1 h. The mass concentrations were determined gravimetrically, and the elemental analysis, in which the concentrations of 14 elements were determined, was carried out using the X-ray fluorescence technique. Five sources of pollution were identified: background, steel grinding, metal active gas welding, tungsten inert gas welding, and machining. The sources were identified by positive matrix factorization, a statistical method for source apportionment. The identified sources corresponded well with the work activities in the workshop and with the actual sources described in previous studies. It is shown that positive matrix factorization can be a valuable tool for the identification and characterization of indoor sources.

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.

Single particle mass spectral signatures from on-road and non-road vehicle exhaust particles and their application in refined source apportionment using deep learning

With advances in vehicle emission control technology, updating source profiles to meet the current requirements of source apportionment has become increasingly crucial. In this study, on-road and non-road vehicle particles were collected, and then the chemical compositions of individual particles were analyzed using single particle aerosol mass spectrometry. The data were grouped using an adaptive resonance theory neural network to identify signatures and establish a mass spectral database of mobile sources. In addition, a deep learning-based model (DeepAerosolClassifier) for classifying aerosol particles was established. The objective of this model was to accomplish source apportionment. During the training process, the model achieved an accuracy of 98.49 % for the validation set and an accuracy of 93.36 % for the testing set. Regarding the model interpretation, ideal spectra were generated using the model, verifying its accurate recognition of the characteristic patterns in the mass spectra. In a practical application, the model performed hourly source apportionment at three specific field monitoring sites. The effectiveness of the model in field measurement was validated by combining traffic flow and spatial information with the model results. Compared with other machine learning methods, our model achieved highly automated source apportionment while eliminating the need for feature selection, and it enables end-to-end operation. Thus, in the future, it can be applied in refined and online source apportionment of particulate matter.

Source apportionment of suspended particulate organic matter in a shallow eutrophic lake of Southwest China using MixSIAR model

Eutrophication has become prominent in many lakes of the world, resulting in a continuous rise of suspended particulate organic matter (POM) that can be of vital consequence for water quality and carbon cycling. While the accumulation and decomposition of POM can enhance nutrient cycling at a rate depending on the property of lake-water organic carbon, it is important to identify the source of suspended POM for evaluating eutrophication and carbon burial. Here we analyzed the distribution of stable isotopic ratios (δ13C and δ15N) and the carbon/nitrogen ratio (C/N) in suspended POM in a shallow eutrophic lake of subtropical China, for apportioning the POM sources using Bayesian mixing model. Specifically, we collected seasonal samples of lake-water POM (>0.7 μm) from 20 sites in Yilong Lake from September 2020 to June 2022, as well as six types of modern samples covering the main carbon sources in the catchment. The spatial surveys showed that in the dry year (low water level) the δ13C of POM was more enriched in the western basin than those in the central and eastern basins, while the seasonal surveys showed the most enriched δ13C of POM in September. However, this spatio-temporal heterogeneity disappeared in the wet year (high water level). It was also found that the δ13C, δ15N and C/N values of POM varied significantly with those of phytoplankton across sites and seasons. Application of the MixSIAR model further showed that phytoplankton was the predominant source of POM in Yilong Lake (79.8 ± 13.4 %), but with large annual difference, 68.9 ± 10.3 % in the dry year and 90.7 ± 3.7 % in the wet year. In comparison, the contribution of the other five endmembers was much more moderate. In particular, the proportion of allochthonous organic carbon in POM was relatively low, with terrestrial plants and soils contributing 6.1 ± 4.8 % and 4.3 ± 2.5 % of POM, respectively. Our quantitative evidence for the dominance of the autochthonous source (i.e., phytoplankton) in lake-water POM over time and space suggested a determining role of eutrophication in the composition of lake organic carbon. Therefore, the coupling of algal blooms with organic carbon cycling in inland waters can be enhanced with continuous catchment development and regional warming.

Long-term trends and source apportionment of fine particulate matter (PM2.5) and gaseous pollutants in Auckland, New Zealand

Airborne particles and pollutant gases are of increasing concern due to their adverse health effects, necessitating a thorough understanding of their composition, sources, spatial and temporal trends for effective air quality management. This study is part of a source apportionment study in Auckland, a dynamic urban environment with complex air quality challenges in an isolated Southern Ocean setting. Over the 2006–2016 period, concentrations of PM2.5, CO, NO2, and SO2 consistently decreased at all 4 monitoring sites indicating the impacts of control measures. The sources impacting the four sites were identified using the positive matrix factorisation (PMF) receptor model. Common sources affecting these sites included motor vehicles (both petrol and diesel), biomass burning, sea salt, sulphate/marine diesel, and soil/road dust. While motor vehicle emissions and biomass burning emerged as the primary contributors to PM2.5, BC, NO2, CO, and SO2, motor vehicle contributions declined due to advancements in fuel formulation and engine technology despite increased vehicle numbers. Biomass burning contributed substantially to winter PM2.5 concentrations driven by domestic heating practices. However, the introduction of alternative heating technologies mitigated the upward trends in biomass burning emissions despite an increasing residential population. Soil/road dust contributions varied by site, influenced by meteorological conditions and local activities, with implications for site-specific air quality management. Sulphate/marine diesel concentrations exhibited seasonal variability, reflecting the impact of both shipping emissions and natural sources. Urban sulphate concentrations decreased due to regulations requiring the introduction of low-sulphur automotive gasoline and diesel fuels. Sea salt, a naturally occurring source, posed challenges for management efforts with concentrations trending downwards over time, possibly linked to climate patterns. This study's source apportionment analysis provides critical insights into Auckland's air quality. The findings inform policy development, air quality management, and health impact assessments, benefiting both the Auckland region and New Zealand as a whole. Ongoing monitoring and emissions reduction strategies, particularly targeting motor vehicles and biomass burning, are pivotal for enhancing air quality and public health in the region.

Source apportionment of suspended particulate matter (PM1, PM2.5 and PM10) collected in road and tram tunnels in Krakow, Poland.

Here, we present the results of a comprehensive study of air quality in two tunnels located in the city of Krakow, southern Poland. The study comprised three PM fractions of suspended particulate matter (PM1, PM2.5 and PM10) sampled during campaigns lasting from March 14 to April 24, 2016 and from June 28 to July 18, 2016, in the road tunnel and the tram tunnel, respectively. The collected samples had undergone comprehensive chemical, elemental and carbon isotope analyses. The results of these analyses gave the basis for better characterization of urban transport as a source of air pollution in the city. The concentrations of particulate matter varied, depending on the analysed PM fraction and the place of sampling. For the tram tunnel, the average concentrations were 53.2µg·m-3 (PM1), 73.8µg·m-3 (PM2.5), 96.5µg·m-3 (PM10), to be compared with 44.2µg·m-3, 137.7µg·m-3, 221.5µg·m-3, respectively, recorded in the road tunnel. The isotope-mass balance calculations carried out separately for the road and tram tunnel and for each PM fraction, revealed that 60 to 79% of carbon present in the samples collected in the road tunnel was associated with road transport, to be compared with 15-33% obtained in the tram tunnel. The second in importance were biogenic emissions (17-21% and 41-49% in the road and tram tunnel, respectively. Sixteen different polycyclic aromatic hydrocarbons (PAHs) have been identified in the analysed samples. As expected, much higher concentrations of PAHs were detected in the road tunnel when compared to the tram tunnel. Based on the analysed PAHs concentrations, health risk assessment was determined using 3 different types of indicators: carcinogenic equivalent (CEQ), mutagenic equivalent (MEQ) and toxic equivalent (TEQ).

Spatial source apportionment of airborne coarse particulate matter using PMF-Bayesian receptor model

Ambient particulate matter (PM2.5 and PM10), has been extensively monitored in numerous urban areas across the globe. Over the past decade, there has been a significant improvement in PM2.5 air quality, while improvements in PM10 levels have been comparatively modest, primarily due to the limited reduction in coarse particle (PM2.5–10) pollution. Unlike PM2.5, PM2.5–10 predominantly originates from local emissions and is often characterized by pronounced spatial heterogeneity. In this study, we utilized over one million data points on PM concentrations, collected from >100 monitoring sites within a Chinese megacity, to perform spatial source apportionment of PM2.5–10. Despite the widespread availability of such data, it has rarely been employed for this purpose. We employed an enhanced positive matrix factorization approach, capable of handling large datasets, in conjunction with a Bayesian multivariate receptor model to deduce spatial source impacts. Four primary sources were successfully identified and interpreted, including residential burning, industrial processes, road dust, and meteorology-related sources. This interpretation was supported by a considerable body of prior knowledge concerning emission sources, which is usually unavailable in most cases. The methodology proposed in this study demonstrates significant potential for generalization to other regions, thereby contributing to the development of air quality management strategies.

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.

Identifying source apportionment of atmospheric particulate matter and gaseous pollutants using receptor models : A case study of Bengaluru, India

The data of Particulate matter PMs (PM2.5, PM10) and Gaseous Pollutants such as carbon monoxide (CO), methane (CH4), oxides of nitrogen (NOx: NO and NO2), non-methane hydrocarbons (NMHCs), sulfur dioxide (SO2), along with ammonia (NH3) at five different locations across Bengaluru from 1st January, 2017 to 20th March, 2018 were collected. The primary objective of this research work is to identify the sources of atmospheric particulate matter and gaseous pollutants using receptor models in Bengaluru, India. To execute this, receptor models, namely Conditional Bivariate Probability Function (CBPF) and Concentrated Weighted Trajectory (CWT) Analysis, are applied. Conditional Bivariate Probability Function (CBPF) shows that, annually, the maximum concentrations of PMs over receptor sites were detected during low wind speed (&lt; 2 knots) along the north-east direction specifying that the long-range transport does not play an essential role in the transportation of higher concentrations of PM and their primary source region may be localized. Concentrated Weighted Trajectory (CWT) analysis shows that, seasonally, the highest air mass contribution of about 37% was noticed in summer, whereas the lowest was in the post-monsoon season (13%). The significant contribution of PM2.5 transported from long distances was during monsoon, and in the case of PM10, it was in summer. The study suggests that the long-range transport of PMs and gaseous Pollutants was not vital and was observed to be localized.

Source apportionment of fine particulate matter at different underground sites in the Chengdu metro system in summer

With the popularization of subway systems, subway air quality has gradually begun to attract public attention. The removal of PM2.5 based solely on concentration may not be sustainable or effective. Targeted measures directed at the emission sources of PM2.5 are therefore needed to improve removal efficiency. The quantification of the chemical composition of PM2.5 is the basis for source apportionment analysis. In this study, PM2.5 samples were collected from tunnels, carriages, platforms, and outdoor environments. The composition of PM2.5 was detected. PM2.5 sources were determined using positive matrix factorization. The average PM2.5 concentrations at subway sites followed the decreasing sequence of tunnel > carriage > platform, and were higher than those outdoors. Iron was the most abundant element in PM2.5 in the subway system. The five PM2.5 sources at subway sites, included train movement, vehicle emission, mixed source (secondary sulfate and coal combustion), secondary nitrate, and industrial emission. After analyzing the differences in PM2.5 sources at various subway sites, train movement and vehicle emission were identified as priority sources to control if we are to improve subway air quality. They are selected in particular mainly owing to their important contributions to PM2.5 release (20.4 %–33.3 % and 18.9 %–32.9 %, respectively). Overall, these findings provide unique insights for the development of targeted strategies to control subway air pollutants, thus helping to achieve healthy and sustainable subway environments.

Seasonal Variations in Chemical Composition and Source Apportionment of Fine Particulate Matter (PM1) in an Urban Site of Jaipur City, Rajasthan

The present study was conducted with an aim to investigate the seasonal variation in mass concentration of fine particulate matter (PM1), their chemical composition, (water and soluble and non-soluble ions and other elements) and gaseous pollutants (SO2, NO2, CO and O3) at an urban site of Jaipur city in India. During summers, pollutants showed a reduction of 18.97%, 41.95%, 32.32% and 20.56% for PM1, NO2, SO2 and CO, respectively. In contrast, O3 showed an increase of 27%. The substantial reduction was also observed in the levels of secondary aerosols SO4 2- (71.15%), NO3-(21.86%), Cl- (65.63%) and K+ (8.16%). The elemental components Al, B, Be, Ca, Cu, Fe, Mn, Ni, and Pb showed a reduction in the range of 15.13 % (Al) to 71% (Cu). On the contrary, an increment was found in the levels of Ag (21.95%), Cd (62.5%), Cr (89.74%), Mg (10.43%) and Na (25.32%). Four factors were extracted by principal component analysis (PCA). In addition to the local sources, three stationary sources have been identified as contributing to the pollution load located in the WNW, NE and ENE direction of the site. It can be concluded that pollutant concentration and chemical composition of any area not only depend on the local emission, but nearby stationary sources and meteorology are significant contributors.

Diurnal source apportionment of organic and inorganic atmospheric particulate matter at a high-altitude mountain site under summer conditions (Sierra Nevada; Spain)

High-altitude mountain areas are sentinel ecosystems for global environmental changes such as anthropogenic pollution. In this study, we report a source apportionment of particulate material with an aerodynamic diameter smaller than 10 μm (PM10) in a high-altitude site in southern Europe (Sierra Nevada Station; SNS (2500 m a.s.l.)) during summer 2021. The emission sources and atmospheric secondary processes that determine the composition of aerosol particles in Sierra Nevada National Park (Spain) are identified from the concentrations of organic carbon (OC), elemental carbon (EC), 12 major inorganic compounds, 18 trace elements and 44 organic molecular tracer compounds in PM10 filter samples collected during day- and nighttime. The multivariate analysis of the joint dataset resolved five main PM10 sources: 1) Saharan dust, 2) advection from the urbanized valley, 3) local combustion, 4) smoke from a fire-event, and 5) aerosol from regional recirculation with high contribution of particles from secondary inorganic and organic aerosol formation processes. PM sources were clearly associated with synoptic meteorological conditions, and day- and nighttime circulation patterns typical of mountainous areas. Although a local pollution source was identified, the contribution of this source to PM10, OC and EC was small. Our results evidence the strong influence of middle- and long-range transport of aerosols, mainly from anthropogenic origin, on the aerosol chemical composition at this remote site.

Oxidative potential and source apportionment of size-resolved particles from indoor environments: Dithiothreitol (DTT) consumption and ROS production

Oxidation potential (OP) is an emerging index to measure the toxicity of atmospheric particulate matter (PM), but little research data on indoor PM OP are available. This study focuses on the size-resolved particle OP and source apportionment of PM from indoor environments. Different-size PM samples were gathered from March 6, 2022, to April 18, 2022, from offices, canteens, dormitories, and laboratories using Anderson samplers. Particles from these indoor environments were separated into nine size fractions, and the chemical components (including transition metals, ions, and water-soluble organic carbon (WSOC)) were measured. The water-soluble OP (OPws) of the investigated PM was highest in the dormitory (61.02 nmol min−1 μg−1) and lowest in the canteen (25.58 nmol min−1 μg−1), varying by factors of up to 2.38. Additionally, the particulate Mn (r2 = 0.94), Fe (r2 = 0.93), and Zn (r2 = 0.92) concentrations were closely related to OPws. The OPws distribution in indoor PM samples featured a single peak from 0.7 to 3.2 μm. ROS (·OH) showed the same trend (1.1–3.2 μm) as PM OP, which shows that PM3.2 is the main contributor to adverse health effects among all size fractions of PM due to the strengthened oxidative ability of PM3.2 compared to PM > 3.2. The source apportionment results showed that secondary sources (52.3%), construction sources (28%), vehicle emissions (18.5%), and dust sources (1.2%) were the main sources of indoor PM OP. Multiple path particle dosimetry (MPPD) model tests evidenced that PM3.2 control the lung OPws and ROS (accounting for >71.49% and 68.07%, respectively) deposition because of the high deposition efficiency and metal concentration in the alveolar region. The experimental trend of OP was closely related to that of ROS. Overall, since OPws is directly associated with diverse health effects for PM OP, special attention to PM3.2 impact.

Chemical Characterization and Source Apportionment of PM10 Using Receptor Models over the Himalayan Region of India

This study presents the source apportionment of coarse-mode particulate matter (PM10) extracted by 3 receptor models (PCA/APCS, UNMIX, and PMF) at semi-urban sites of the Indian Himalayan region (IHR) during August 2018–December 2019. In this study, water-soluble inorganic ionic species (WSIIS), water-soluble organic carbon (WSOC), carbon fractions (organic carbon (OC) and elemental carbon (EC)), and trace elements of PM10 were analyzed over the IHR. Nainital (62 ± 39 µg m−3) had the highest annual average mass concentration of PM10 (average ± standard deviation at 1 σ), followed by Mohal Kullu (58 ± 32 µg m−3) and Darjeeling (54 ± 18 µg m−3). The annual total ∑WSIIS concentration order was as follows: Darjeeling (14.02 ± 10.01 µg m−3) &gt; Mohal-Kullu (13.75 ± 10.21 µg m−3) &gt; Nainital (10.20 ± 6.30 µg m−3), contributing to 15–30% of the PM10 mass. The dominant secondary ions (NH4+, SO42−, and NO3−) suggest that the study sites were strongly influenced by anthropogenic sources from regional and long-range transport. Principal component analysis (PCA) with an absolute principal component score (APCS), UNMIX, and Positive Matrix Factorization (PMF) were used for source identification of PM10 at the study sites of the IHR. All three models showed relatively similar results of source profiles for all study sites except their source number and percentage contribution. Overall, soil dust (SD), secondary aerosols (SAs), combustion (biomass burning (BB) + fossil fuel combustion (FFC): BB+FFC), and vehicular emissions (VEs) are the major sources of PM10 identified by these models at all study sites. Air mass backward trajectories illustrated that PM10, mainly attributed to dust-related aerosols, was transported from the Thar Desert, Indo-Gangetic Plain (IGP), and northwestern region of India (i.e., Punjab and Haryana) and Afghanistan to the IHR. Transported agricultural or residual burning plumes from the IGP and nearby areas significantly contribute to the concentration of carbonaceous aerosols (CAs) at study sites.

Characterization and Source Apportionment of PM in Handan—A Case Study during the COVID-19

Handan is a typical city affected by regional particulate pollution. In order to investigate particulate matter (PM) characterization, source contributions and health risks for the general populations, we collected PM samples at two sites affected by a pollution event (12–18 May 2020) during the COVID-19 pandemic and analyzed the major components (SNA, OCEC, WSIIs, and metal elements). A PCA-MLR model was used for source apportionment. The carcinogenic and non-carcinogenic risks caused by metal elements in the PM were assessed. The results show that the renewal of old neighborhoods significantly influences local PM, and primarily the PM10; the average contribution to PM10 was 27 μg/m3. The source apportionment has indicated that all other elements came from dust, except Cd, Pb and Zn, and the contribution of the dust source to PM was 60.4%. As PM2.5 grew to PM10, the PM changed from basic to acidic, resulting in a lower NH4+ concentration in PM10 than PM2.5. The carcinogenic risk of PM10 was more than 1 × 10−6 for both children and adults, and the excess mortality caused by the renewal of the community increased by 23%. Authorities should pay more attention to the impact of renewal on air quality. The backward trajectory and PSCF calculations show that both local sources and short-distance transport contribute to PM—local sources for PM10, and short-distance transport in southern Hebei, northern Henan and northern Anhui for PM2.5, SO2 and NO2.

Source apportionment of fine particulate matter at a megacity in China, using an improved regularization supervised PMF model

The source apportionment of particulate matter plays an important role in solving the atmospheric particulate pollution. Positive matrix factorization (PMF) is a widely used source apportionment model. At present, high resolution online datasets are increasingly rich, but acquiring accurate and timely source apportionment results is still challenging. Integrating prior knowledge into modelling process is an effective solution and can yield reliable results. This study proposed an improved source apportionment method for the regularization supervised PMF model (RSPMF). This method leveraged actual source profile to guide factor profile for rapidly and automatically identifying source categories and quantifying source contributions. The results showed that the factor profile from RSPMF could be interpreted as seven factors and approach to actual source profile. Average source contributions were also an agreement between RSPMF and EPAPMF, including secondary nitrate (26 %, 27 %), secondary sulfate (23 %, 24 %), coal combustion (18 %, 18 %), vehicle exhaust (15 %, 15 %), biomass burning (10 %, 9 %), dust (5 %, 4 %), industrial emission (3 %, 3 %). The solutions of RSPMF also exhibited good generalizability during different episodes. This study reveals the superiority of supervised model, this model embeds prior knowledge into modelling process to guide model for obtaining more reliable results.

Air Pollution in South Texas: A Short Communication of Health Risks and Implications

Air pollution is a major public health concern. The region of South Texas in the United States has experienced high levels of air pollution in recent years due to an increase in population, cross-border trade between the U.S.A. and Mexico, and high vehicular activity. This review assesses the relationships between human health and air pollution in South Texas. A thorough scientific search was performed using PubMed, Science Direct, and ProQuest, with most of the literature focusing on the source apportionment of particulate matter that is 2.5 microns or less in width (PM2.5), Carbon Dioxide (CO2), carbon monoxide (CO), Black Carbon (BC), and associated health risks for children and pregnant women. Findings from the source apportionment studies suggest the role of industries, automobiles emissions, agricultural burning, construction work, and unpaved roads in the overall deterioration of air quality and deleterious health effects, such as respiratory and cardiovascular diseases. This review demonstrates the pressing need for more air pollution and health effects studies in this region, especially the Brownsville–Harlingen–McAllen metropolitan area.