Source identification and apportionment of PM2.5 and PM2.5-10 in iron and steel scrap smelting factory environment using PMF, PCFA and UNMIX receptor models

Spatio-temporal distribution and source contributions of the ambient pollutants in Lucknow city, India

Clean air is imperative to the survival of all life forms on the planet. However, recent times have witnessed enormous escalation in urban pollution levels. It is therefore, incumbent upon us to decipher measures to deal with it. In perspective, the present study was carried out to assess PM10 and PM2.5 loading, metallic constituents, gaseous pollutants, source contributions, health impact and noise level of nine-locations, grouped as residential, commercial, and industrial in Lucknow city for 2019-21. Mean concentrations during pre-monsoon for PM10, PM2.5, SO2 and NO2 were: 138.2 ± 35.2, 69.1 ± 13.6, 8.5 ± 3.3 and 32.3 ± 7.4 µg/m3, respectively, whereas post-monsoon concentrations were 143.0 ± 33.3, 74.6 ± 14.5, 12.5 ± 2.1, and 35.5 ± 6.3 µg/m3, respectively. Exceedance percentage of pre-monsoon PM10 over National Ambient Air Quality Standards (NAAQS) was 38.2% while that for post-monsoon was 43.0%; whereas corresponding values for PM2.5 were 15.2% and 24.3%. Post-monsoon season showed higher particulate loading owing to wintertime inversion and high humidity conditions. Order of elements associated with PM2.5 is Co < Cd < Cr < Ni < V < Be < Mo < Mn < Ti < Cu < Pb < Se < Sr < Li < B < As < Ba < Mg < Al < Zn < Ca < Fe < K < Na and that with PM10 is Co < Cd < Ni < Cr < V < Ti < Be < Mo < Cu < Pb < Se < Sr < Li < B < As < Mn < Ba < Mg < Al < Fe < Zn < K < Na < Ca. WHO AIRQ + ascertained 1654, 144 and 1100 attributable cases per 0.1 million of population to PM10 exposure in 2019-21. Source apportionment was carried out using USEPA-PMF and resolved 6 sources with highest percent contributions including road dust re-entrainment, biomass burning and vehicular emission. It is observed that residents of Lucknow city regularly face exposure to particulate pollutants and associated constituents making it imperative to develop pollution abetment strategies.

Source and risk assessment of heavy metals in mining-affected areas in Jiangxi Province, China, based on Monte Carlo simulation

In recent years, heavy metal contamination of soils has become a major concern in China due to the potential risks involved. To assess environmental pollution and human health risks in a typical heavy metal polluted site in Jiangxi Province, a thorough evaluation of the distribution, pollution levels, and sources of heavy metals in soils of the Yangmeijiang River watershed was conducted in this study. Positive matrix factorization and Monte Carlo simulation were used to evaluate the ecological and human health risks of heavy metals. The research findings indicate that heavy metal pollution was the most severe at the depth of 20-40 cm in soils, with local heavy metal pollution resulting from mining and sewage irrigation. The high-risk area accounted for 91.11% of the total area. However, the pollution level decreased with time due to sampling effects, rainfall, and control measures. Leaf-vegetables and rice were primarily polluted by Cd and Pb. The main four sources of heavy metals in soils were traffic emission, metal smelting, agricultural activities and natural sources, mining extraction, and electroplating industries. Heavy metals with the highest ecological risk and health risk are Cd and As, respectively. The non-carcinogenic and carcinogenic risks of children were 7.0 and 1.7 times higher than those of adults, respectively. Therefore, children are more likely to be influenced by heavy metals compared to adults. The results obtained by the risk assessments may contribute to the identification of specific sources of heavy metals (e.g., traffic emissions, metal smelting, mining excavation, and electroplating industries). Additionally, the environmental impacts and biotoxicity associated with various heavy metals (e.g., Cd and As) can also be reflected. These outcomes may serve as a scientific basis for the pollution monitoring and remediation in the mining-affected areas.

Source identification and health risk assessment of PM2.5 in urban districts of Hanoi using PCA/APCS and UNMIX

Comparing results obtained by different models with different physical assumptions and constraints for source apportionment is important for better understanding the sources of pollutants. Source apportionment of PM2.5 measured at three sites located in inner urban districts of Hanoi was performed using two receptor models, UNMIX and principal component analysis with absolute principle component score (PCA/APCS). A total of 78 daily samples were collected consecutively during the dry and wet seasons in 2019 and 2020. The average PM2.5 concentration (66.26 µg/m3 ± 29.70 µg/m3 with a range from 23.57 to 169.04 µg/m3) observed in Hanoi metropolitan exceeded the National Ambient Air Quality standard QCVN 05:2013/BTNMT (50 µg/m3). Both UNMIX and PCA/APCS expressed comparable ability to reproduce measured PM2.5 concentrations. Additionally, both models identified similar potential sources of PM2.5 including traffic-related emissions, scrap metal recycling villages, crustal mixed with construction sources, coal combustion mixed with industry, and biomass burning. Both UNMIX and PCA/APCS confirmed that traffic-related emission was the most influential PM2.5 with a high percentage contribution of 59% and 55.97%, respectively. All the HQ and Cr values for both children and adults of toxic elements apportioned by both UNMIX and PCA/APCS in every source were within the acceptable range.

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 site of Faridabad, IGDTUW-Delhi and CSIR-NPL of NCR-Delhi were observed to be 195 ± 121, 275 ± 141 and 209 ± 81 µg m-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 PM10 were entrenched to the receptor models to comprehend the possible sources of 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).

Insights the dominant contribution of biomass burning to methanol-soluble PM2.5 bounded oxidation potential based on multilayer perceptron neural network analysis in Xi'an, China

Atmospheric fine particulate matter (PM2.5) is associated with cardiorespiratory morbidity and mortality due to its ability to generate reactive oxygen species (ROS). Ambient PM2.5 samples were collected during heating and nonheating seasons in Xi'an, China, and the ROS-generation potential of PM2.5 was quantified using the dithiothreitol (DTT) assay. Additionally, positive matrix factorization combined with multilayer perceptron was employed to apportion sources contributing to the oxidation potential of PM2.5. Both the mass concentration of PM2.5 and the volume-based DTT activity (DTTv) were higher during the heating season than during the nonheating season. The primary contributors to DTTv were combustion (biomass and coal) sources during the heating season (>52 %), whereas secondary formation dominated DTT activity during the nonheating season (35.7 %). In addition, the secondary reaction process promoted the generation of intrinsic oxidation potential (OP) of sources. Among all the sources investigated (traffic source, industrial emission, mineral dust, biomass burning, secondary formation and coal combustion), the inherent oxidation potential of biomass burning was the highest, whereas that of mineral dust was the lowest. Our study indicates that anthropogenic sources, especially biomass burning, should be prioritized in PM2.5 toxicity control strategies.

Contrasting compositions of PM2.5 in Northern Thailand during La Niña (2017) and El Niño (2019) years

There have been a very limited number of systematic studies on PM2.5 compositions and their source contribution in Southeast Asia. This study aims to explore the characteristics of PM2.5 composition collected in Chiang Mai (Thailand) during La Niña and El Niño years and to apportion their sources during smoke haze and non-haze periods. The average PM2.5 concentration of smoke haze episode in 2019 (El Niño) was much higher than in 2017 (La Niña). The ratios of organic carbon (OC) to elemental carbon (EC), as well as K (biomass burning (BB) tracer) to PM2.5, were higher during smoke haze episodes in 2019 than in 2017 indicating a significant influence from BB. The ratios of secondary organic carbon (SOC) levels to primary organic carbon (POC) levels during smoke haze episodes were higher than those in non-haze period, which indicated greater SOC contributions or more photo-oxidation of precursors in haze episodes with high ambient temperatures. However, the ratios of soil markers (Ca and Mg) during non-haze period were high implying that soil source contributed more to PM2.5 concentrations when there less BB occurred. The positive Matrix Factorization (PMF) model revealed that the source of BB, characterized by high K fractions, was the largest contributor during smoke haze episodes accounting for 50% (2017) and 79% (2019). Climate conditions influence meteorological patterns, particularly during incidences of extreme weather such as droughts, which affect the scale and frequency of open burning and thus air pollution levels.

Characteristics, sources and health risk assessment of trace metals and polycyclic aromatic hydrocarbons in PM2.5 from Hefei, China

Trace metals (TRs) and polycyclic aromatic hydrocarbons (PAHs) are major toxic components of fine particulate matter (PM2.5) and related to various health adverse outcomes. The study aims to get a better understanding of the contents, sources and risks of PM2.5-bounded TRs and PAHs in Hefei, China, during the period of 2019-2021. We collected 504 samples and measured twelve TRs and sixteen priority PAHs by inductively coupled plasma mass spectrometry and high-performance liquid chromatography. The annual mass concentrations of PM2.5 was fluctuated in the year of 2019-2021 at 50.95, 47.48 and 59.38 μg/m3, with seasonal variations in rank order of winter > spring > autumn > summer. The median concentrations of PM2.5-bounded ƩTRs and ƩPAHs were also fluctuated, 132.85, 80.93 and 120.27 ng/m3 for ƩTRs, 2.57, 5.85 and 2.97 ng/m3 for ƩPAHs, in the year of 2019, 2020 and 2021, respectively. Seasonal variations of ƩTRs and ƩPAHs show the highest concentration in winter. Positive matrix factorization was used for identified pollution emission sources, and TRs mainly originated from coal combustion, traffic emission and fugitive dust, while PAHs stemmed from biomass, diesel, gasoline and coal combustion. Health risk assessment indicated that adults were more vulnerable than children, the carcinogenic risk assessment of As and Cr manifested a certain degree of cancer risk (1.0 × 10-6 < CR < 1.0 × 10-4) in adults group, and health risks of TRs were higher than PAHs in Hefei. These findings suggest that PM2.5-bounded TRs and PAHs should be considered when making emission control strategies for air pollution, and winter, combustion sources and adults should achieve more policy attention to decrease exposure risks in Hefei.

Quantitative source apportionment and driver identification of soil heavy metals using advanced machine learning techniques

The accurate identification of pollution sources is essential for the prevention and control of possible pollution from soil heavy metals (SHMs). However, the positive matrix factorisation (PMF) model has been widely used as a conventional method for pollution source apportionment, and the classification of source apportionment results mainly relies on existing research and expert experience, which can result in high subjectivity in the source interpretation. To address this limitation, a comprehensive source apportionment framework was developed based on advanced machine learning techniques that combine self-organizing mapping and PMF with a gradient boosting decision tree (GBDT) model. Analysis of Cd, Pb, Zn, Cu, Cr, and Ni in 272 topsoils showed that the average contents of six heavy metals were 1.72-13.79 times greater than corresponding background values, among which Cd pollution was relatively serious, with 66.91 % of the sites having higher values than the specified soil risk screening values. The PMF results revealed that 79.43 % of Pb was related to vehicle emissions and atmospheric deposition, 79.32 % of Cd and 38.84 % of Zn were related to sewage irrigation, and 85.97 % of Cr and 85.50 % of Ni were from natural sources. Moreover, the GBDT detected that industrial network density, water network density, and Fe₂O₃ content were the major drivers influencing each pollution source. Overall, the novelty of this study lies in the development of an improved framework based on advanced machine learning techniques that led to the accurate identification of the sources of SHM pollution, which can provide more detailed support for environmental protection departments to propose targeted control measures for soil pollution.

Source Apportionment and Model Applicability of Heavy Metal Pollution in Farmland Soil Based on Three Receptor Models

The identification of the source of heavy metal pollution and its quantification are the prerequisite of soil pollution control. The APCS-MLR, UNMIX and PMF models were employed to apportion pollution sources of Cu, Zn, Pb, Cd, Cr and Ni of the farmland soil in the vicinity of an abandoned iron and steel plant. The sources, contribution rates and applicability of the models were evaluated. The potential ecological risk index revealed greatest ecological risk from Cd. The results of source apportionment illustrated that the APCS-MLR and UNMIX models could verify each other for accurate allocation of pollution sources. The industrial sources were the main sources of pollution (32.41~38.42%), followed by agricultural sources (29.35~31.65%) and traffic emission sources (21.03~21.51%); and the smallest proportion was from natural sources of pollution (11.2~14.42%). The PMF model was easily affected by outliers and its fitting degree was not ideal, leading to be unable to get more accurate results of source analysis. The combination of multiple models could effectively improve the accuracy of pollution source analysis of soil heavy metals. These results provide some scientific basis for further remediation of heavy metal pollution in farmland soil.

Polycyclic aromatic hydrocarbons in road dusts of a densely populated African city: spatial and seasonal distribution, source, and risk assessment

Road dust is a principal source and depository of polycyclic aromatic hydrocarbons (PAHs) in many urban areas of the world. Hence, this study probed the spatial and seasonal pattern, sources, and related cancer health risks of PAHs in the road dusts sampled at ten traffic intersection (TIs) of a model African city. Mixed PAHs sources were ascertained using the diagnostic ratios and positive matrix factorization (PMF) model. The results showed fluctuations in mean concentrations from 36.51 to 43.04 µg/g. Three-ring PAHs were the most abundant PAHs with anthracene (Anth) ranging from 6.84 ± 1.99 to 9.26 ± 4.42 µg/g being the predominant pollutant in Ibadan. Benzo(k)Fluoranthene (BkF) which is a pointer of traffic emission was the most dominant among the seven carcinogenic PAHs considered, varying from 2.68 ± 0.43 to 4.59 ± 0.48 µg/g. Seasonal variation results showed that PAH concentrations were 20% higher during dry season than rainy season. The seven sources of PAHs identified by PMF model include the following: diesel vehicle exhausts, gasoline combustion, diesel combustion, coal tar combustion, gasoline vehicle exhausts, coal and wood (biomass) combustion, and emissions from unburnt fossil fuels. Employing the incremental lifetime cancer risk (ILCR) model, the city's cancer risk of 5.96E-05 for children and 6.60E-05 for adults were more than the satisfactory risk baseline of ILCR ≤ 10^-6 and higher in adults than in Children.

Source identification and health risk assessments of heavy metals in indoor dusts of Ilorin, North central Nigeria

BACKGROUND AND PURPOSE

Exposure to heavy metals (HMs) in indoor dusts is a serious public concern that is linked to a myriad of deleterious health outcomes. The objectives of this study are to estimate the contamination levels of HMs in indoor dusts of different residential areas in Ilorin, Nigeria; identify HMs sources in different residential areas; and evaluate human health risks of HMs in selected residential areas.

METHODS

Indoor dust sampling was conducted in ten randomly selected from low, medium and high population density residential areas of Ilorin, Nigeria. Ten HMs concentration levels, their health risk implication and the associated potential ecological risks were evaluated.

RESULTS

The mean concentration levels measured for Fe, Pb, Zn, As, Co, Cr, Cu, Cd, Mn and Ni were 38.99, 5.74, 3.99, 0.08, 2.82, 2.13, 0.47, 0.60, 6.45 and 1.09 mg/kg, respectively. Positive Matrix Factorization (PMF) model was applied to ascertain sources of HMs in sampled indoor dust. Percentage contribution from oil-based cooking (29.82%) and transportation (29.77%) represented the highest source to HM concentrations among the six factors identified. The results of the various pollution indices employed showed that Pb, Zn, As, Co, Cr, Cu, Mn and Ni contributed moderately to HMs concentration levels in the sampled dusts. Cd had highest potential ecological risk factor E r i of between 160 and 320. The average values of Enrichment Factors (EFs) obtained aside from Fe used as the reference metal, ranged between 8.46 (As) and 2521.61(Cd). Health risk assessment results revealed that children are the most susceptible to the risks associated with HMs bound indoor dust than the adults. The percentage risk contributions of Hazard Quotient via ingestion route (HQ_ing) in Hazard Index (HI) for non-cancer risk of indoor HMs were 93.17% and 69.87% in children and adults, respectively. Likewise, the percentage cancer risks contribution through ingestion pathway (CR_ing) were higher than cancer risks through inhalation and dermal pathways (CR_inh and CR_dermal), accounting for 99.84% and 97.04% of lifetime cancer risk in children and adults, respectively. The contamination level of Cd recorded is of great concern and signifies very strong contribution from anthropogenic sources.

CONCLUSION

This study has further revealed the levels of HMs in typical African residential settings that could be used by relevant stakeholders and policy makers in developing lasting control measures.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40201-021-00778-8.

How do socioeconomic factors influence urban PM2.5 pollution in China? Empirical analysis from the perspective of spatiotemporal disequilibrium

PM_2.5 pollution has harmed the health and social lives of residents, and although evidence of PM_2.5 pollution caused by human activities has been reported in a large body of literature, traditional econometric and spatial models can explain the contribution of a given factor from only a global perspective. Given this limitation, this study quantitatively investigated the effects of the spatiotemporal heterogeneity of various socioeconomic factors on PM_2.5 pollution in 273 Chinese cities from 2010 to 2016 by exploratory spatial data analysis (ESDA) and geographically weighted regression (GWR). The spatiotemporal distribution pattern and intrinsic driving mechanism of city-level PM_2.5 pollution were systematically examined. The results indicate the following: (1) The cities with high PM_2.5 pollution are located north of the Yangtze River and east of the Hu line. A notable positive spatial correlation was observed between these cities, and nearly one-third of the cities are in the HH clustering area. (2) From the global regression point of view, population size and economic development are the main factors causing the deterioration and spread of PM_2.5 pollution in Chinese cities, and population size undoubtedly exerts the strongest influence. Industrial structure, economic development, openness degree, urbanization and road intensity also play weak roles in promoting urban PM_2.5 pollution. (3) The socioeconomic factors influencing pollution exhibit significant spatial heterogeneity. Specifically, the cities in which pollution is promoted by economic development are mainly concentrated in the northeast and western regions. The cities in which population size exerts a positive driving effect are in most regions, except for a few central and western cities. Three targeted strategies for developing more sustainable cities are comprehensively discussed by building on the understanding of the socioeconomic driving mechanism for PM_2.5 pollution.

Removal of lead and other toxic metals in heavily contaminated soil using biodegradable chelators: GLDA, citric acid and ascorbic acid

In this study, we investigated the level of contamination of agricultural soil near an old recycling lead smelter in Vietnam and proposed an effective treatment for the remediation of the soil. The analysis of soil samples using an ICP-MS method revealed that the soil in the area was heavily contaminated by heavy metals, especially lead (Pb) with concentrations in surface soil of >3000 μg g^-1. High concentrations of metals, including Pb, copper (Cu) and zinc (Zn), were found in whole soil profile. The FE-EPMA and Laser-Raman spectrometer results suggested that iron minerals and carbon materials in the soil are the important hosts of the toxic metals. Subsequently, a series of washing experiment were performed on the soil using biodegradable chelators, including N, N-dicarboxymethyl glutamic acid tetrasodium salt (GLDA), ascorbic acid and citric acid. The results showed that the mixture of GLDA-ascorbic (100 mM: 100 mM) can be considered as a potential candidate for Pb and Zn removal, which removes approximately 90% of Pb and 70% of Zn. Meanwhile, a mixture of GLDA-citric would be preferred for Cu removal based on its greater extraction efficiency compared to other mixtures.

Disturbance mechanisms of lacustrine organic carbon burial: Case study of Cuopu Lake, Southwest China

Lakes are important organic carbon (OC) traps in the global carbon cycle. Recent studies have shown that the rate of OC burial in lacustrine sediments is influenced by factors such as climate change, land-use change, and eutrophication. In this study, we use multiproxy methods to reveal the mechanisms of lacustrine sediment OC burial in an alpine lake (Cuopu Lake), in southwest China. Combined with the dating from ²¹⁰Pb_ex and n-alkanes distribution analysis using the Positive Matrix Factorization model, the sedimentary history was divided into five stages: religious activity (the 1840s-1880s), earthquake (the 1880s-1910s), garrison (the 1910s-1960s), transition (the 1960s-1990s), and ecotourism (the 1990s-2010s). During the earthquake stage, OC burial was dominated by terrestrial solids (>40%) and co-precipitated algae (>30%), with a rapid deposition rate (>4 mm a^-1) and low OC concentration (<4 mg g^-1). During the other stages, when the level of disturbance was relatively low, a change in nutrient conditions either promoted or inhibited plant growth, which influenced the type of buried OC. The contribution of OC derived from combustion sources varied from stage to stage. Severe anthropogenic disturbances have led to a significant increase in nutritional levels in the lake water, leading to an increase in the OC burial rate. Climate change, which leads to changes in temperature and rainfall, did not significantly influence OC burial, whereas nitrogen deposition (and associated ecological changes) was a significant determinant. When the general mechanism is dominant, the total nitrogen to inorganic phosphorus ratio is an effective indicator of OC burial due to its selective promotion of different plant types. In conclusion, our results suggest that lacustrine sediment OC burial is closely linked to physical and anthropogenic factors in Cuopu Lake, as well as similar montane lakes.

Seasonal characteristics of aerosols (PM2.5 and PM10) and their source apportionment using PMF: A four year study over Delhi, India

The present study attempts to explore and compare the seasonal variability in chemical composition and contributions of different sources of fine and coarse fractions of aerosols (PM_2.5 and PM₁₀) in Delhi, India from January 2013 to December 2016. The annual average concentrations of PM_2.5 and PM₁₀ were 131 ± 79 μg m^-3 (range: 17-417 μg m^-3) and 238 ± 106 μg m^-3 (range: 34-537 μg m^-3), respectively. PM_2.5 and PM₁₀ samples were chemically characterized to assess their chemical components [i.e. organic carbon (OC), elemental carbon (EC), water soluble inorganic ionic components (WSICs) and heavy and trace elements] and then used for estimation of enrichment factors (EFs) and applied positive matrix factorization (PMF5) model to evaluate their prominent sources on seasonal basis in Delhi. PMF identified eight major sources i.e. Secondary nitrate (SN), secondary sulphate (SS), vehicular emissions (VE), biomass burning (BB), soil dust (SD), fossil fuel combustion (FFC), sodium and magnesium salts (SMS) and industrial emissions (IE). Total carbon contributes ∼28% to the total PM_2.5 concentration and 24% to the total PM₁₀ concentration and followed the similar seasonality pattern. SN and SS followed opposite seasonal pattern, where SN was higher during colder seasons while SS was greater during warm seasons. The seasonal differences in VE contributions were not very striking as it prevails evidently most of year. Emissions from BB is one of the major sources in Delhi with larger contribution during winter and post monsoon seasons due to stable meteorological conditions and aggrandized biomass burning (agriculture residue burning in and around the regions; mainly Punjab and Haryana) and domestic heating during the season. Conditional Bivariate Probability Function (CBPF) plots revealed that the maximum concentrations of PM_2.5 and PM₁₀ were carried by north westerly winds (north-western Indo Gangetic Plains of India).

Water quality assessment and pollution source apportionment in a highly regulated river of Northeast China

Dams and sluices break down the river continuum, alter the river hydrological regime, and intercept the migration processes of nutrients and pollutants. The regulation of dams and sluices will have great impacts on water quality characteristics in the river basin. In this study, variable fuzzy pattern recognition model (VFPR), principal component analysis/factor analysis (PCA/FA), and the absolute principal component score-multiple linear regression (APCS-MLR) were used to assess the water quality and identify the potential pollution sources in a highly regulated river of Northeast China. A set of water quality variables at three stations were measured from January 2015 to August 2017. The water quality assessment results showed that there were spatial and temporal variations of water quality and the total nitrogen (TN) and fecal coliforms (F. coli) were the major pollution factors of the study river section. Four pollution sources, including industrial effluent source, domestic sewage source, meteorological factor and atmospheric deposition source, and agricultural non-point source, were identified in dry and wet seasons using the PCA/FA method. The APCS-MLR results showed that the industrial effluent source was the main pollution source in dry seasons and had a decrease in wet seasons. While the mean contribution of the domestic sewage source had an increase in wet seasons, influenced by the sewage overflow and the flushing of pollutants during the extreme precipitation, the construction of dams decreased the flow obviously in wet seasons and increased in dry seasons. The increase in pollutants caused by storm runoff and the reduction of dilution water in the river channel could be the main reason for the water quality degradation in wet seasons.

Trace element characterization of fine particulate matter and assessment of associated health risk in mining area, transportation routes and institutional area of Dhanbad, India

Samples of PM_2.5 were collected on PTFE filters at 11 monitoring stations in Dhanbad, India, from March, 2014, to February, 2015, for the quantification of 10 PM_2.5-bound trace elements by using ICP-OES, source apportionment by using principal component analysis and health risks posed by PM_2.5-bound trace elements by using health risk assessment model developed by US EPA. The average annual PM_2.5 concentration (149 ± 66 µg/m³) exceeded the national ambient air quality standards by factor of 3.7, US EPA national ambient air quality standards by factor of 10 and WHO air quality guidelines by factor of 15. The sum total of average annual concentration of all PM_2.5-bound trace elements was found to be 3.206 µg/m³ with maximum concentrations of Fe (61%), Zn (21%) and Pb (11%). Coal mining, coal combustion, vehicular emission, tyre and brake wear and re-suspension of road dust were identified as dominant sources of PM_2.5-bound trace elements from the results of correlation and chemometric analysis. The significantly high HQ values posed by PM_2.5-bound Co and Ni and intensification of HI values (15.7, 10.8 and 8.54 in mining area, transportation routes and institutional area, respectively) for multielemental exposure indicate high potential of non-carcinogenic health risk associated with inhalation exposure. The carcinogenic health risk due to multielemental exposure in mining area (2.27 × 10^-4) and transportation routes (1.57 × 10^-4) for adults were significantly higher than threshold value indicating the vulnerability of adults toward inhalation-induced carcinogenic risk posed by PM_2.5-bound trace elements.

Biomonitoring of environmental pollution in the vicinity of iron and steel smelters in southwestern Nigeria using transplanted lichens and mosses

This study identified specific emission sources of atmospheric pollution in the vicinity of two secondary iron and steel smelting factories in Osun state, southwestern Nigeria, using transplanted biomonitors. A total of 120 biomonitors consisting of lichen and moss were grown under a controlled environment and later transplanted to the surroundings of each factory for monitoring of air pollutants for 3 months in both wet and dry seasons. The elemental contents (K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Br, Rb and Sr) of the biomonitors were determined by X-ray florescence (XRF) spectroscopy. The source identification was performed by applying positive matrix factorization (PMF) receptor modelling approach using the elemental data set from the two smelters. Among the measured elements, Fe had the highest average concentration in the lichen and moss samples as well as in both seasons. The average concentrations of Co, Ni, Cu, Zn, As and Br were low. The varying average elemental concentrations of lichen and moss reflect the pattern of impact of smelting on atmospheric airborne pollution around the factories. The four factors resolved by PMF and their respective contributions were metal processing (39.0%), Fe source (28.0%), crustal/soil (22.0%) and road dust (11.0%) for moss and Fe source (34.0%), crustal/soil (26.0%), coal combustion (25.0%) and road dust (15.0%) for lichen. The study showcases lichen and moss as cheaper and yet efficient uninterrupted monitoring tools of air pollution sources associated with iron and steel smelting industrial activities.

Using multi-site data to apportion PM-bound metal(loid)s: Impact of a manganese alloy plant in an urban area

The identification and quantification of the PM emission sources influencing a specific area is vital to better assess the potential health effects related to the PM exposure of the local population. In this work, a multi-site PM₁₀ sampling campaign was performed in seven sites located in the southern part of the Santander Bay (northern Spain), an urban area characterized by the proximity of some metal(loid) industrial sources (mainly a manganese alloy plant). The total content of V, Mn, Fe, Ni, Cu, Zn, As, Mo, Cd, Sb and Pb was determined by ICP-MS. This multi-site dataset was evaluated by positive matrix factorization (PMF) in order to identify the main anthropogenic metal(loid) sources impacting the studied area, and to quantify their contribution to the measured metal(loid) levels. The attribution of the sources was done by comparing the factor profiles obtained by the PMF analysis with representative profiles from known metal(loid) sources in the area, included in both the European database SPECIEUROPE (V2.0) and the US database EPA-SPECIATE (V4.5) or calculated from literature data. In addition, conditional bivariate probability functions (CBPF)s were used to assist in the identification of the sources. Four metal(loid) sources were identified: Fugitive and point source emissions from the manganese alloy plant (49.9% and 9.9%, respectively), non-exhaust traffic emissions (38.3%) and a minor source of mixed origin (1.8%). The PMF analysis was able to make a clear separation between two different sources from the manganese alloy plant, which represented almost 60% of the total measured metal(loid) levels, >80% of these emissions being assigned to fugitive emissions. These results will be useful for the assessment of the health risk associated with PM₁₀-bound metal(loid) exposure and for the design of efficient abatement strategies in areas impacted by similar industries.

Source Apportionment of PM10 Over Three Tropical Urban Atmospheres at Indo-Gangetic Plain of India: An Approach Using Different Receptor Models

The present work is the ensuing part of the study on spatial and temporal variations in chemical characteristics of PM₁₀ (particulate matter with aerodynamic diameter ≤ 10 μm) over Indo Gangetic Plain (IGP) of India. It focuses on the apportionment of PM₁₀ sources with the application of different receptor models, i.e., principal component analysis with absolute principal component scores (PCA-APCS), UNMIX, and positive matrix factorization (PMF) on the same chemical species of PM₁₀. The main objective of this study is to perform the comparative analysis of the models, obtained mutually validated outputs and more robust results. The average PM₁₀ concentration during January 2011 to December 2011 at Delhi, Varanasi, and Kolkata were 202.3 ± 74.3, 206.2 ± 77.4, and 171.5 ± 38.5 μg m^-3, respectively. The results provided by the three models revealed quite similar source profile for all the sampling regions, with some disaccords in number of sources as well as their percent contributions. The PMF analysis resolved seven individual sources in Delhi [soil dust (SD), vehicular emissions (VE), secondary aerosols (SA), biomass burning (BB), sodium and magnesium salt (SMS), fossil fuel combustion, and industrial emissions (IE)], Varanasi [SD, VE, SA, BB, SMS, coal combustion, and IE], and Kolkata [secondary sulfate (Ssulf), secondary nitrate, SD, VE, BB, SMS, IE]. However, PCA-APCS and UNMIX models identified less number of sources (besides mixed type sources) than PMF for all the sampling sites. All models identified that VE, SA, BB, and SD were the dominant contributors of PM₁₀ mass concentration over the IGP region of India.

Heavy metals in industrially emitted particulate matter in Ile-Ife, Nigeria

Iron and steel smelting facilities generate large quantities of airborne particulate matter (PM) through their various activities and production processes. The resulting PM that contains a variety of heavy metals has potentially detrimental impacts on human health and the environment. This study was conducted to assess the potential health effects of the pollution from the heavy metals in the airborne PM sampled in the vicinity of secondary smelting operations in Ile-Ife, Nigeria. X-ray fluorescence (XRF) was used to determine the elemental concentration of Pb, Cr, Cd, Zn, Mn, As, Fe, Cu, and Ni in the size-segregated PM samples. Pollution Indices (PI) consisting of Contamination Factor (CF), Degree of Contamination (DC) and Pollution Index Load (PLI) and Target Hazard Quotient (THQ) were employed to assess the pollution risk associated with the heavy metals in the PM. CF, DC and PLI values were 3< CF <6, >32 and >1, respectively for the three sites, indicating deterioration of the ambient air quality in the vicinity of the smelter. The heavy metals in the airborne PM pose a severe health risk to people living in vicinity of the facility and to its workers. The diminished air quality with the associated health risks directly depends on the industrial emissions from steel production and control measures are recommended to mitigate the likely risks.

Chemical characteristics and source apportionment of PM2.5 using PCA/APCS, UNMIX, and PMF at an urban site of Delhi, India

The present study investigated the comprehensive chemical composition [organic carbon (OC), elemental carbon (EC), water-soluble inorganic ionic components (WSICs), and major & trace elements] of particulate matter (PM_2.5) and scrutinized their emission sources for urban region of Delhi. The 135 PM_2.5 samples were collected from January 2013 to December 2014 and analyzed for chemical constituents for source apportionment study. The average concentration of PM_2.5 was recorded as 121.9 ± 93.2 μg m^-3 (range 25.1-429.8 μg m^-3), whereas the total concentration of trace elements (Na, Ca, Mg, Al, S, Cl, K, Cr, Si, Ti, As, Br, Pb, Fe, Zn, and Mn) was accounted for ∼17% of PM_2.5. Strong seasonal variation was observed in PM_2.5 mass concentration and its chemical composition with maxima during winter and minima during monsoon seasons. The chemical composition of the PM_2.5 was reconstructed using IMPROVE equation, which was observed to be in good agreement with the gravimetric mass. Source apportionment of PM_2.5 was carried out using the following three different receptor models: principal component analysis with absolute principal component scores (PCA/APCS), which identified five major sources; UNMIX which identified four major sources; and positive matrix factorization (PMF), which explored seven major sources. The applied models were able to identify the major sources contributing to the PM_2.5 and re-confirmed that secondary aerosols (SAs), soil/road dust (SD), vehicular emissions (VEs), biomass burning (BB), fossil fuel combustion (FFC), and industrial emission (IE) were dominant contributors to PM_2.5 in Delhi. The influences of local and regional sources were also explored using 5-day backward air mass trajectory analysis, cluster analysis, and potential source contribution function (PSCF). Cluster and PSCF results indicated that local as well as long-transported PM_2.5 from the north-west India and Pakistan were mostly pertinent.