Conditional Probability Function Research Articles

Source apportionment of PM2.5 episodes in the Taichung metropolitan area, Taiwan

To analyze the physicochemical mechanisms affecting the variation in fine suspended particulate matter (PM2.5) concentrations in Taichung City, the largest city in central Taiwan (the second largest city in Taiwan), during a high-pollution event from November 3 to 6, 2021, we applied the sulfur tracking method (STM) and integrated source apportionment method (ISAM) of the WRF/CMAQ model to simulate the impacts of various emission sources. The sources of pollution in Taichung City are very similar, which shows that the impacts of point, line, and area sources should not be neglected in addition to the boundary conditions. SO42− is mainly generated from point emissions and the production of H2O2, Fe, Mn, and O3. NO3− is also mainly generated from point sources in Taichung City, with HNO3 being the main source at noon and ANO3 at other times of the day. NH4+ is mainly generated from area sources in Taichung City. OM is more complex, mainly originating from line sources in Taichung City and other sources, such as point/area emissions in Taichung City and other emissions from Changhua County. The most important mechanism is low-volatility/semivolatile oxidized combustion of OC at noon, followed by low-volatility/semivolatile POA, which is produced in the morning or evening. EC mainly originates from line sources in Taichung City and Changhua County. In other nearby counties, EC is dominated by local emission sources. In addition, when the concentration of PM2.5 is high, the Neutralization Ratio (NR) is high and PM2.5 is relatively neutral or slightly alkaline. On the contrary, when the concentration of PM2.5 is low, the NR is lower than 1 and the aerosol is acidic. Besides, this study used positive matrix factorization (PMF), which indicates that the PM2.5 at the UAPRS originated from eight kinds of pollution, namely, windblown dust, oil cracking, iron and steel industry, sea salt, transport, Cl− containing exhaust, biomass burning, fossil combustion containing abundant SO42− and the heavy oil refining and coal combustion industry. The direction of the source of pollution can be traced by a conditional bivariate probability function (CBPF). Overall, fossil fuel combustion, mainly involving sulfate, is the largest source of pollution, with the heavy oil refining and coal combustion industry contributing less, and the remaining factors contribute relatively evenly.

Characteristics and Source Analysis of VOCs Pollution During Emergency Response in Tianjin

To elucidate the characteristics of VOCs chemical components during heavy pollution episodes, hourly online VOCs data derived from 11 heavy pollution events in Tianjin from 2019 to 2020 were employed. The positive matrix factorization （PMF） and conditional bivariate probability function （CBPF） were employed to analyze the sources of VOCs during heavy pollution episodes. The results indicated that the average VOCs volume fraction during these episodes was recorded at 35.7×10-9. Furthermore, it was observed that during the winter emergency response period, there was a discernible increase in the volume fraction of VOCs when compared to that during the autumn season. Specifically, there was a notable upswing of 48% in the olefins category, whereas alkanes registered a 4% increase. Additionally, the VOCs component structure changed significantly during the heavy pollution episodes. During the orange warning period, the proportion of alkanes increased by 36%, and the proportion of acetylene decreased by 32%. During the yellow warning period, the proportion of alkanes increased by 14%, and the proportion of acetylene decreased by 5%. During the emergency response period, motor vehicle emission sources, natural gas evaporative sources, and solvent use sources were the main contributors of VOCs in environmental receptors, contributing 17.5%, 15.4%, and 15.2%, respectively. Compared with that during the period antecedent to the emergency response, the contribution of vehicle emission sources and diesel volatile sources to VOCs in environmental receptors decreased by 2.0% to 5.5% and 2.1% to 6.6%, respectively, and the contribution of solvent use sources decreased by 0.2% to 2.4% during the yellow warning period. During the orange warning period, the contribution of motor vehicle emission sources was reduced by 0.1% to 8.3%, and the contribution of solvent use sources was reduced by 0.5% to 6.2%.

Random telegraph processes with nonlocal memory.

We study two-state (dichotomous, telegraph) random ergodic continuous-time processes with dynamics depending on their past. We take into account the history of the process in an explicit form by introducing integral nonlocal memory term into conditional probability function. We start from an expression for the conditional transition probability function describing additive multistep binary random chain and show that the telegraph processes can be considered as continuous-time interpolations of discrete-time dichotomous random sequences. An equationinvolving the memory function and the two-point correlation function of the telegraph process is analytically obtained. This integral equationdefines the correlation properties of the processes with given memory functions. It also serves as a tool for solving the inverse problem, namely for generation of a telegraph process with a prescribed pair correlation function. We obtain analytically the correlation functions of the telegraph processes with two exactly solvable examples of memory functions and support these results by numerical simulations of the corresponding telegraph processes.

Sequential three-way group decision-making for double hierarchy hesitant fuzzy linguistic term set

Group decision-making (GDM) characterized by complexity and uncertainty is an essential part of various life scenarios. Most existing researches lack tools to fuse information quickly and interpret decision results for partially formed decisions. This limitation is particularly noticeable when there is a need to improve the efficiency of GDM. To address this issue, a novel multi-level sequential three-way decision for group decision-making (S3W-GDM) method is constructed from the perspective of granular computing. This method simultaneously considers the vagueness, hesitation, and variation of GDM problems under double hierarchy hesitant fuzzy linguistic term sets (DHHFLTS) environment. First, for fusing information efficiently, a novel multi-level expert information fusion method is proposed, and the concepts of expert decision table and the extraction/aggregation of decision-leveled information based on the multi-level granularity are defined. Second, the neighborhood theory, outranking relation and regret theory (RT) are utilized to redesign the calculations of conditional probability and relative loss function. Then, the granular structure of DHHFLTS based on the sequential three-way decision (S3WD) is defined to improve the decision-making efficiency, and the decision-making strategy and interpretation of each decision-level are proposed. Furthermore, the algorithm of S3W-GDM is given. Finally, an illustrative example of diagnosis is presented, and the comparative and sensitivity analysis with other methods are performed to verify the efficiency and rationality of the proposed method.

Development of the compound intensity measure and seismic performance assessment for aqueduct structures considering fluid-structure interaction

The primary motivation of this investigation is to develop the compound ground motion intensity measure (IM) for the seismic performance assessment of aqueduct structures. To achieve this goal, taking the typical aqueduct structure in Southwestern China as a case study, a three-dimensional finite element model of aqueduct-water-foundation fully coupling system is established. Moreover, for the seismic hazard, a total of 300 as-recorded ground motions based on the design response spectrum are selected from PEER-NGA strong ground motion database, including near-field non-pulse (NFNP), near-field pulse-type (NFP) and far-field (FF). Then, the nonlinear correlation between single IMs is considered by Copula function, the compound IM is proposed that can comprehensively characterize the seismic performance of the aqueduct structure. Based on evaluation criteria, the applicability of the proposed compound IM is compared and verified. Finally, the conditional probability function and the unconditional probabilistic seismic risk analysis is employed to evaluate the seismic performance of aqueduct structures. It can be concluded that the compound IM considers the acceleration, velocity and displacement factors, which improves the correlation, efficiency and proficiency of probabilistic seismic demand analysis. Furthermore, the compound IM can help to improve the accuracy of the structural seismic performance assessment and reduce the uncertainty of structural response estimation. In summary, the findings of this study highlight the significance of proposing the compound IM and developing fragility curves when evaluating the seismic performance of aqueduct structures.

Dual-isotope ratios of carbonaceous aerosols for seasonal observation and their assessment as source indicators

Carbonaceous aerosols exhibit seasonal variations due to a complex interplay of emission sources, meteorological conditions, and chemical processes. This study presents the first year-round dual‑carbon isotopic analysis of carbonaceous aerosols in Northeastern Europe (Lithuania). The emphasis was placed on the processes affecting carbonaceous submicron particle (PM1) concentrations and their isotopic composition (δ13CTC, fc) during different seasons. Aerosol particles were collected in the two distinct sites: at an urban background site (Vilnius) and a coastal site (Preila). The concentrations of total carbon (TC) and black carbon (BC) varied both spatially and temporally. The annual average concentrations were 4 μg/m3 for TC and 2.3 μg/m3 for BC at the urban background site. They were considerably lower at the coastal site with 2.9 μg/m3 for TC and 0.74 μg/m3 for BC. The peak concentrations of TC and BC that occur during the cold season indicate a significant impact from residential heating. The δ13C in aerosols exhibited a distinct seasonal cycle with depleted δ13CTC values during the warm season (April–October). Through the integration of isotopic composition, contemporary carbon (fc), and BC source apportionment, we achieved precise predictions of isotopic parameter changes, encompassing pollution sources and the influence of meteorological parameters. To better understand the respective contributions of local and regional sources, air mass trajectories, wind patterns (speed and direction), and the polar conditional probability function (CPF) were studied in parallel. The study indicates that the isotopic composition of PM1 at both sites is primarily controlled by emission sources (local and regional), while meteorological conditions (temperature and mixing layer height) have less influence. These variations have important implications for regional air quality, climate dynamics, and public health, which are persistently subject to continuous research and monitoring.

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.

An Analysis of Primary Contributing Sources to the PM2.5 Composition in a Port City in Canada Influenced by Traffic, Marine, and Wildfire Emissions

Ambient air quality, specifically fine particulate matter (PM2.5) pollution, poses a global health concern due to its associations with adverse health effects and persistent presence in urban settings. This study investigates PM2.5 exposure in a traffic-influenced residential area in Vancouver, employing chemical speciation data sampled in the vicinity of a major road throughout 2018. Using receptor-oriented models, Positive Matrix Factorization (PMF), and Principal Component Analysis (PCA), major emission sources contributing to the total PM2.5 mass were identified. The Organic and Elemental carbon concentration time series analysis reveals impacts from traffic and episodic influences from wildfires and residential heating. Applying PMF, we attribute identified factors to specific sources: traffic (33%), secondary aerosols (27%), dust (14%), Marine Heavy Fuel Combustion (HFC) (12%), Local (9%), and rare metallic elements (5%), respectively. This apportioning is subsequently verified by PCA, which introduces a sea salt factor to the analysis. Applying the conditional probability function analysis on the integrated wind speed and direction, and the PM concentration data further confirms the partial PM mass apportionment to marine HFC, rare metallic elements, and local factors. Lastly, comparing the results with the 2015 emission inventory of Vancouver shows similarities in apportioning the share of primary sources while overlooking secondary emissions in the latter, emphasizing the importance of field sampling for accurate source characterization. This study highlights the necessity of receptor modeling coupled with field sampling in quantifying the contribution and chemical characteristics of secondary emission sources, particularly near residential neighborhoods. Precise characterization aids health exposure studies that contribute to informed air quality management and policy regulations.

Toxic elements, sources and health risk assessment of PM2.5 in an industrial area of Surabaya, Indonesia

ABSTRACT A comprehensive study on air quality in the industrial area of Indonesia, which significantly impacts respiratory health and poses breathing hazards, is still lacking. This study aims to uncover the contents of toxic elements, identify potential sources of fine particles, and evaluate their contributions to health risks in Surabaya’s industrial area. The study focuses on identifying chemical composition, sources, and assessing health risks for both children and adults. The PM2.5 concentration from 2021 to 2022 ranged from 3.29 to 27.05 µg m−3, with an average value of 13.63 ± 4.54 µg m−3, exceeding the WHO’s annual standard of 5 µg m−3. Black carbon (BC) accounted for 27% of PM2.5 composition. A seasonal variation was found; the highest PM2.5 and BC were observed during the dry season, with the highest peak in June. The highest concentration of elements in PM2.5 was observed for S, followed by Si, K, Zn, Fe, and Pb, with maximum values of 3.53, 1.16, 1.01, 1.86, 0.29, and 0.37 µg m−3, respectively. Source apportionment using positive matrix factorisation (PMF) revealed eight sources: galvanising industry, ammonium chloride, secondary sulphate, biomass burning emission, soil, steel industry, vehicle emissions, and lead smelting, with metal industries accounting for 37.3%. Investigation into their potential source locations was conducted using the conditional bivariate probability function (CBPF) method. Human health assessment for adults and children indicates carcinogenic risks with a total risk of 9.50 × 10−5 and 2.38 × 10−5, respectively. The results emphasise the significance of managing sources linked to industry and transportation-related sources, crucial in decreasing high PM2.5 levels and several elevated elements and mitigating the health impact in Surabaya’s industrial area, Indonesia.

Bayesian network approach for dynamic fault tree with common cause failures and interval uncertainty parameters

Traditional fault tree analysis often assumes that the basic events are independent and the failure parameters are known. Therefore, it is powerless to deal with the correlation among basic events and the uncertainty of failure parameters due to the small failure data. Therefore, a framework based on continuous-time Bayesian network is proposed to evaluate the reliability of fault tree with common cause failures (CCF) and uncertainty parameters. Firstly, the best-worst method (BWM) and hesitant fuzzy set (HFS) are introduced to address the issue of β-factor being influenced by experts’ subjectivity. Then, the interval theory is introduced to deal with the uncertainty parameters. Based on continuous-time Bayesian network, the conditional probability functions of logic gates (i.e. AND gate, OR gate, spare gate, priority AND gate) with CCF are derived, and the upper and lower bounds of failure probability of top event can be solved. Finally, the fault tree of CPU system is given to verify the effectiveness of the proposed framework.

Source apportionment of PM2.5 using DN-PMF in three megacities in South Korea

AbstractPM2.5 pollution is problematic in megacities on the western coast in South Korea (Seoul, Incheon, and Gwangju). As these megacities are located downwind of China, their air quality is easily affected by local and long-range transport sources. PM2.5 samples collected in Seoul (n = 222), Incheon (n = 221), and Gwangju (n = 224) from September 2020 to March 2022, were chemically characterized. Dispersion normalized positive matrix factorization was applied to these PM2.5 speciated data to provide source apportionments. Nine common sources (including secondary nitrate, secondary sulfate, biomass burning, mobile, and waste incinerator) were identified at all sites. The conditional bivariate probability function helped to identify each site’s local sources. Joint potential source contribution function analysis identified northeast China and Inner Mongolia as potential source areas of long-range transport pollutants affecting all sites. Forced lifestyle changes due to the pandemic such as limited gatherings while increased recreational activities may have caused different patterns on the biomass burning source. The constraints on old vehicles during the policy implementation periods likely reduced the mobile source contributions in cities that adopted the policy. Secondary nitrate accounted for 40% of the PM2.5 mass at all sites, implying a significant impact from NOX sources. While the current policy focuses primarily on controlling primary emission sources, it should include secondary sources as well which may include precursor emissions control. Healthier air quality would be achieved if the policy effects are not limited to local, but also to foreign sources in regions upwind of Korea by intergovernmental collaboration.

Particulate black carbon mass concentrations and the episodic source identification driven by atmospheric blocking effects in Astana, Kazakhstan

Black carbon (BC) is a component of fine particulate matter (PM2.5) that is a key contributor to adverse human health effects and climate forcing. To date, BC mass concentrations and possible sources in Kazakhstan have not been studied. Thus, understanding the temporal variations of BC for a large developing region with a complex climate is useful. In this study, measurements of fine particulate BC mass concentrations in Astana were made from June 2020 to October 2021 by measuring light absorption of PM2.5 on filters. The mean BC concentration was 2.56 ± 1.29 μg m−3 with maximum and minimum monthly mean BC concentrations being 4.56 ± 2.03 μg m−3 and 1.12 ± 0.42 μg m−3 in January 2021 and June 2020, respectively. Temporal analyses of BC, SO2, PM10, NOx, CO, meteorological and atmospheric stability parameters were performed. Aggregated pollutant ‘episodic loadings’ during the heating and non-heating periods were identified. Their relationships with blocking anticyclones and cyclones were investigated by examining the reversal of meridional gradients at 500 hPa geopotential height (GPH) maps and identifying Omega (Ω) and Rex blocking types. Astana has some of the highest BC concentrations of cities worldwide. Seasonal BC source location identification using Conditional Bivariate Probability Function (CBPF) analysis implicated combined heat and power (CHP) plant emissions as the major BC source in Astana. Significant increases in BC concentrations were observed during the cold season due to numerous sources, generally poorer atmospheric dispersion and blocking events. The Concentration Weighted Trajectory (CWT) analysis results showed that the distribution of the 75th percentile of BC during episodic periods actively controlled by blockings exceeding than the entire measurement period, which may reflect cross-border transport and adjacent countries.

Source identification of ultrafine particulate matter and ammonia emissions in the metropolitan area

Previous studies have estimated the sources of particulate matter in the atmosphere. Among these, studying the sources of secondary aerosols harmful to human health is important. However, there is a lack of research on ammonia (NH3), a precursor to secondary aerosol formation. This study uses positive matrix factorization (PMF) model and conditional bivariate probability function (CBPF) model to estimate the sources of particulate matter and ammonia. The results showed that about 40% of the PM2.5 mass at both sites was attributable to secondary aerosol. To estimate the emission sources of ammonia that contribute to the generation of secondary aerosols, CBPF was utilized to model and compare the emission characteristics of categorized pollution sources and ammonia, and it was found that SMA had similar emission trends to industry, road dust, oil combustion, and biomass combustion, while GRA had similar emission trends to oil combustion and vehicle (diesel). Considering the results from these two regions, ammonia in the metropolitan area is more likely to be emitted from daily activities than from long distances. The study results demonstrate the major role of secondary aerosols on ambient PM2.5 concentrations and can help develop effective management strategies and policies for air pollution mitigation.

Attribution of aerosol particle number size distributions to main sources using an 11-year urban dataset

Abstract. Source apportionment was performed using size-segregated atmospheric particle number concentrations (PNCs) in 27 size channels over a diameter range of 6–1000 nm augmented by air pollutants with a time resolution of 1 h in Budapest for 11 full years. The input dataset was treated for the effect of the local meteorology using dispersion correction. Both the uncorrected dataset and corrected dataset were evaluated using positive matrix factorization for separate seasons. Six source types including nucleation, two road vehicle emission sources separated into a semi-volatile fraction and a solid-core fraction, a diffuse urban source, a secondary inorganic aerosol (SIA) source, and an ozone-associated secondary aerosol source were identified, characterized and quantified. The dispersion correction did not considerably change the profiles, diel variations or patterns of the sources, while it substantially modified the relative shares of the nucleation source in all seasons. The mean relative contributions of the traffic emissions (60 %) indicate that on-road motor vehicles were the leading source of particle numbers. The nucleation was responsible for 24 % of the PNC annually as a lower estimate. It exhibited a compound character consisting of photochemically induced nucleation and traffic-related nucleation. Its contributions were the highest in spring and the lowest in winter. The shares of the urban diffuse and SIA source types were the largest in autumn and winter and in spring and summer, respectively, but they were typically ⪅ 10 %. The O3-associated secondary aerosol made up the smallest (⪅ 3 %) contributions. The conditional bivariate probability function analysis showed considerable spatial variations in the source origin. The combination of the size-segregated particle number concentrations, wide overall range of the size channels, considerably long dataset, dispersion correction and modelling over separate seasons led jointly to a unique adaptation of the source apportionment and yielded novel and valuable insights into the urban aerosol sources and processes both for Budapest and in general.

Day–night variation, size distribution, source identification, and health risk of polycyclic aromatic hydrocarbons in particulate matter in Ulsan, South Korea

Particulate matter (PM) in 11 different sizes was collected at a semi-rural site in Ulsan, a multi-industrial city in South Korea, over one year from 2019 to 2020 to investigate the temporal patterns in size distribution and potential sources of polycyclic aromatic hydrocarbons (PAHs). The mean concentration of Σ21 PAHs was higher in winter (0.29 and 0.45 ng/m3 for coarse and fine PM, respectively) than in summer (0.06 and 0.07 ng/m3). Although there was no statistically significant difference between day and night for the concentration of individual PAHs in both coarse and fine PM, the concentrations were generally higher during the day than at night. During the day, Σ21 PAHs had two distinct peaks (0.056–0.10 μm and 3.20–5.60 μm) in winter, but they tended to partition to larger particles (1.00–1.80 μm and >18.0 μm) in summer. This result suggests that the volatilization of PAHs in smaller particles was enhanced by high-temperature conditions in summer, followed by their condensation onto larger particles. At night, Σ21 PAH peaks were mainly around the size of 0.7 μm (droplet mode), suggesting the importance of heterogeneous-aqueous reactions. The results of the conditional bivariate probability function and concentration-weighted trajectory indicate that the high concentrations of PAHs in summer and winter were more affected by local pollution and long-range atmospheric transport, respectively. The results of the health risk assessment demonstrate that PAHs in fine PM contributed significantly to cancer risk compared to those in coarse PM. In conclusion, investigating the size distribution of PAHs in particles is necessary to understand their environmental behaviors and evaluate potential health risks.

Investigation of odor pollution by utilizing selected ion flow tube mass spectrometry (SIFT-MS) and principal component analysis (PCA).

Odor pollution, also referred to as odor nuisance, is a growing environmental concern that is significantly associated with mental health. Once emitted into the air, the concentration of odorous substances varies considerably with wind conditions, leading to difficulties in timely sampling. In the present study, we employed selected ion flow tube mass spectrometry (SIFT-MS) to measure 22 odor-producing molecules continuously in an urban-rural complex city. In addition, we applied statistical analyses, principal component analysis (PCA), and a conditional probability function (CPF) to the datasets obtained from SIFT-MS to identify the odor characteristics at two study sites. At site A, odorants related to livestock farming and industry showed high factor loadings on principal components (PCs) from the PCA. In contrast, we estimated that the odorous gaseous chemicals affecting site B were closely related to sewage treatment and municipal solid waste disposal. Similar CPF patterns of grouped substances from the PCA supported the association between potential odor sources and specific odorants at site B, which helped estimate possible source locations. Consequently, our findings indicate that continuous monitoring of odorous substances using SIFT-MS can be an effective way to provide sufficient information on odor-producing molecules, leading to the clear identification of odor characteristics despite the high variability of odorous substances.

Characteristics of Volatile Organic Compounds Distribution in Downtown Ansan Near Industrial Complexes

There are four industrial complexes located around Ansan City, which is one of the areas with the highest VOCs emissions in Gyeonggi region based on the emissions provided by the clean policy support system (CAPSS), Korea. In this study, proton transfer reaction time-of-flight mass spectrometer (PTR-TOF-MS) was used to determine the distribution of volatile organic compounds (VOCs) in urban areas near industrial complexes in Ansan City in 2023 (May to June). The PTR-TOF-MS was operated over the mass range of 10-400amu at a drift tube pressure of 2.3mbar and temperature 80<sup>o</sup>C (E/N ~ 130Td) that enabled the collection of VOC data at 0.1Hz resolution. The mixing ratio calculations for methanol, acetaldehyde, acetonitrile, acetone, acetic acid, dimethyl sulfide, isoprene, methyl vinyl ketone (MVK), methyl ethyl ketone (MEK), benzene, toluene, styrene, xylene, trimethylbenzene and pinene reported in this study were done using the sensitivity factors obtained from the PTR-TOF-MS calibrations. As a result of the intensive measurement in springtime, among the substances constituting VOCs, methanol was the most abundant with 16.27±7.13 ppb, followed by acetone, acetaldehyde, acetic acid, methyl ethyl ketone (MEK), toluene, and xylene. VOCs compositions were dominated by oxidized VOCs, with CxHyO1 at 74%, CxHyO2 at 11%, aromatic CxHy at 10.5%, and CxHyN at 3.1%, respectively. As a result of the diurnal variations and conditional probability function (CPF) analysis, it was found that toluene and MEK were locally greatly influenced by industrial complexes distributed around Ansan City. Related with the ozone creating contribution, the ratio of VOCs to nitrogen oxide (NOx) was 6.5 (1.4 to 18.2), which appeared in the NOx-limited (4:1) and the VOCs-limited (15:1). Therefore it is suggested that the management of emission source NOx and VOCs are very important for the reduction of ozone in Ansan.

Chemical characteristics, morphology and source apportionment of PM10 over National Capital Region (NCR) of India.

The present study frames the physico-chemical characteristics and the source apportionment of PM10 over National Capital Region(NCR) of India using the receptor model's Positive Matrix Factorization (PMF) and Principal Momponent Mnalysis/Absolute Principal Component Score-Multilinear Regression (PCA/APCS-MLR). The annual average mass concentration of PM10 over the urban siteof Faridabad, IGDTUW-Delhi and CSIR-NPL of NCR-Delhi were observed to be 195 ± 121, 275 ± 141 and 209 ± 81µgm-3, respectively. Carbonaceous species (organic carbon (OC), elemental carbon (EC) and water-soluble organic carbon (WSOC)), elemental constituents (Al, Ti, Na, Mg, Cr, Mn, Fe, Cu, Zn, Br, Ba, Mo Pb) and water-soluble ionic components (F-, Cl-, SO42-, NO3-, NH4+, Na+, K+, Mg2+, Ca2+) of PM10were entrenched to the receptor models to comprehend the possible sourcesof PM10. The PMF assorted sources over Faridabad were soil dust (SD 15%), industrial emission (IE 14%), vehicular emission (VE 19%), secondary aerosol (SA 23%) and sodium magnesium salt (SMS 17%). For IGDTUW-Delhi, the sources were SD (16%), VE (19%), SMS (18%), IE (11%), SA (27%) and VE + IE (9%). Emission sources like SD (24%), IE (8%), SMS (20%), VE + IE (12%), VE (15%) and SA + BB (21%) were extracted over CSIR-NPL, New Delhi, which are quite obvious towards the sites. PCA/APCS-MLR quantified the similar sources with varied percentage contribution. Additionally, catalogue the Conditional Bivariate Probability Function (CBPF) for directionality of the local source regions and morphology as spherical, flocculent and irregular were imaged using a Field Emission-Scanning Electron Microscope (FE-SEM).

Vehicular pollution as the primary source of oxidative potential of PM2.5 in Bhubaneswar, a non-attainment city in eastern India.

We assessed the oxidative potential (OP) of PM2.5 (n = 230) using dithiothreitol (DTT) assay to identify the major emission sources in Bhubaneswar (20.20°N, 85.80°E), one of the non-attainment cities under the National Clean Air Program, situated on the eastern coast of India. Continuous day and night PM2.5 samples were collected during periods influenced by marine airmass (MAM; April-May 2019) as well as continental airmass (CAM; October 2019-December 2019). Volume normalized DTT (DDTv) activities were approximately two times higher during CAM compared to MAM periods. In contrast, mass normalized DTT activity (DDTm) showed insignificant variations between CAM and MAM periods. This might be due to particulate organic matter, which accounted for more than one-fifth of the PM2.5 mass loading and remained surprisingly invariant during the study periods. Positive matrix factorization (PMF) identified secondary aerosols (MAM: 26% and CAM: 33%) as dominant contributors to PM2.5 mass in both periods. OP, is, however, dominated by vehicular emissions (21%) as identified through multiple linear regression. Conditional Bivariate Probability Function (CBPF) analysis indicated that local sources were the primary drivers for the catalytic activity of PM2.5 in the study region. Additionally, stagnant meteorological conditions, combined with the chemical aging of species during regional transport of pollutants, likely enhanced redox activity of PM2.5 during the CAM period. The study highlights that increasing traffic congestion is primarily responsible for adverse health outcomes in the region. Therefore, it is important to regulate mobility and vehicular movement to mitigate the hazardous impact of PM2.5 in Bhubaneswar.

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 (< 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.