Chemical element balances and identification of air pollution sources

An advanced deep learning predictive model for air quality index forecasting with remote satellite-derived hydro-climatological variables

Forecasting the air quality index (AQI) is a critical and pressing challenge for developing nations worldwide. With air pollution emerging as a significant threat to the environment, this study considers seven study sites of the sub-tropical region in Bangladesh and introduces a novel hybrid deep-learning model. The proposed model, expressed as CLSTM-BiGRU, integrates a convolutional neural network (CNN), a long-short term memory (LSTM), and a bi-directional gated recurrent unit (BiGRU) network. Leveraging nineteen remotely sensed predictor variables and harnessing the grey wolf optimization (GWO) algorithm, the CLSTM-BiGRU model showcases its superiority in air quality forecasting. It consistently outperforms the benchmark models, yielding lower forecasting errors and higher efficiency (i.e., correlation coefficient ~1) values. Hence, this study underscores the feasibility and substantial potential of the hybrid deep learning model, which can provide precise forecasts of air quality index, and will be highly useful for relevant stakeholders and decision-makers. Furthermore, the adaptability and potential utility of this innovative model may be ascertained for air quality monitoring and effective public health risk mitigation in urban environments.

Air Pollution-Related Neurotoxicity Across the Life Span

Air pollution is a complex mixture of gases and particulate matter, with adsorbed organic and inorganic contaminants, to which exposure is lifelong. Epidemiological studies increasingly associate air pollution with multiple neurodevelopmental disorders and neurodegenerative diseases, findings supported by experimental animal models. This breadth of neurotoxicity across these central nervous system diseases and disorders likely reflects shared vulnerability of their inflammatory and oxidative stress-based mechanisms and a corresponding ability to produce brain metal dyshomeo-stasis. Future research to define the responsible contaminants of air pollution underlying this neurotoxicity is critical to understanding mechanisms of these diseases and disorders and protecting public health.

Postnatal exposure to ambient air pollutants is associated with the composition of the infant gut microbiota at 6-months of age

Epidemiological studies in adults have shown that exposure to ambient air pollution (AAP) is associated with the composition of the adult gut microbiome, but these relationships have not been examined in infancy. We aimed to determine if 6-month postnatal AAP exposure was associated with the infant gut microbiota at 6 months of age in a cohort of Latino mother-infant dyads from the Southern California Mother's Milk Study (n = 103). We estimated particulate matter (PM_2.5 and PM₁₀) and nitrogen dioxide (NO₂) exposure from birth to 6-months based on residential address histories. We characterized the infant gut microbiota using 16S rRNA amplicon sequencing at 6-months of age. At 6-months, the gut microbiota was dominated by the phyla Bacteroidetes, Firmicutes, Proteobacteria, and Actinobacteria. Our results show that, after adjusting for important confounders, postnatal AAP exposure was associated with the composition of the gut microbiota. As an example, PM₁₀ exposure was positively associated with Dialister, Dorea, Acinetobacter, and Campylobacter while PM_2.5 was positively associated with Actinomyces. Further, exposure to PM₁₀ and PM_2.5 was inversely associated with Alistipes and NO₂ exposure was positively associated with Actinomyces, Enterococcus, Clostridium, and Eubacterium. Several of these taxa have previously been linked with systemic inflammation, including the genera Dialister and Dorea. This study provides the first evidence of significant associations between exposure to AAP and the composition of the infant gut microbiota, which may have important implications for future infant health and development.

Refined source apportionment of residential and industrial fuel combustion in the Beijing based on real-world source profiles

Residential and industrial emissions are considered as dominant contributors to ambient fine particulate matter (PM_2.5) in China. However, the contributions of residential and industrial fuel combustion are difficult to distinguish because specific source indicators are lacking. In this study, real-world source testing was performed on residential coal, biomass and industrial combustion, industrial processes, and diesel and gasoline vehicle source emissions in the Beijing-Tianjin-Hebei region, China. PM_2.5 emission factors and chemical profiles, including 97 compositions (e.g., carbonaceous matter, water-soluble ions, elements, EPA priority polycyclic aromatic hydrocarbons (EPAHs), methyl PAHs (MPAHs), and n-alkanes) were obtained for the aforementioned sources. The results showed high OC1, OC2, fluoranthene, methyl fluoranthene, and retene in emissions from residential coal combustion, high OC3, sulfate, Ca, and iron abundance in emissions from industrial combustion, and high Pb and Zn loadings in emissions from industrial processes. Furthermore, specific diagnostic ratios were determined to distinguish between residential and industrial fuel combustion. For example, the ratios of MPAHs/EPAHs (>1) and Mfluo/Fluo (>5) can be used as fingerprinting ratios to distinguish residential coal combustion from other sources. Finally, 1-h resolution refined source apportionments of PM_2.5 were conducted in Beijing during two haze events (EP1 and EP2) with a chemical mass balance (CMB) model based on the localized real-world source profiles established in this study. Source apportionment results of CMB showed that the contributions of industrial and residential fuel combustion were 19.4% and 30.8% in EP1 and 26.8% and 18.1% in EP2, respectively, which were comparable to the results of the positive matrix factorization model (R² = 0.82). This study provides valuable information for the successful and accurate determination of the contributions of residential and industrial fuel combustion to ambient PM_2.5.

Non-invasive sampling of bats reflects their potential as ecological indicators of elemental exposure in a diamond mining area, northern Limpopo Province, South Africa

Bats have been proposed as reliable bioindicators for monitoring bioaccumulation of elements and chemicals in natural and transformed ecosystems. Non-invasive methods are becoming more popular as research moves away from destructive methodologies. We present the first concentrations of 23 elements in Mops condylurus and Tadarida aegyptiaca (Molossidae) fur and blood from an opencast diamond mine and reference area using inductively coupled plasma mass spectrometry (ICP-MS). Concentrations of B, K, Rb and Cd in the bats' fur were significantly higher in the mining footprint compared to the reference area (P < 0.05). Other elements such as Zn and Hg were significantly higher in the blood of bats from the mining footprint than the reference area (P < 0.05), whereas Mn was significantly higher in the blood of bats from the reference area than from the mining footprint (P < 0.05). Sixteen of the 22 elements above the limit of detection, with the exception of Ba, were significantly higher in the fur samples than in the blood due to elements being incorporated over time into the fur as it grows, whereas blood reveals short-term exposure to elements. Concentrations of most of the elements were reasonably low except Al, Fe and Zn. In general, the element concentrations particularly in the fur samples were comparable with other international studies reporting elemental fur concentrations from anthropogenically impacted and natural areas. Fur and blood have the potential to be viable indicators of environmental toxicity, but research is required on toxic thresholds and physiological and ecological unknowns around element concentrations in bat tissues and organs.

Present cum future of SARS-CoV-2 virus and its associated control of virus-laden air pollutants leading to potential environmental threat - A global review

The world is presently infected by the biological fever of COVID-19 caused by SARS-CoV-2 virus. The present study is mainly related to the airborne transmission of novel coronavirus through airway. Similarly, our mother planet is suffering from drastic effects of air pollution. There are sufficient probabilities or evidences proven for contagious virus transmission through polluted airborne-pathway in formed aerosol molecules. The pathways and sources of spread are detailed along with the best possible green control technologies or ideas to hinder further transmission. The combined effects of such root causes and unwanted outcomes are similar in nature leading to acute cardiac arrest of our planet. To maintain environmental sustainability, the prior future of such emerging unknown biological hazardous air emissions is to be thoroughly researched. So it is high time to deal with the future of hazardous air pollution and work on its preventive measures. The lifetime of such an airborne virus continues for several hours, thus imposing severe threat even during post-lockdown phase. The world waits eagerly for the development of successful vaccination or medication but the possible outcome is quite uncertain in terms of equivalent economy distribution and biomedical availability. Thus, risk assessments are to be carried out even during the post-vaccination period with proper environmental surveillance and monitoring. The skilled techniques of disinfection, sanitization, and other viable wayouts are to be modified with time, place, and prevailing climatic conditions, handling the pandemic efficiently. A healthy atmosphere makes the earth a better place to dwell, ensuring its future lifecycle.

Air Pollution-Related Brain Metal Dyshomeostasis as a Potential Risk Factor for Neurodevelopmental Disorders and Neurodegenerative Diseases

Increasing evidence links air pollution (AP) exposure to effects on the central nervous system structure and function. Particulate matter AP, especially the ultrafine (nanoparticle) components, can carry numerous metal and trace element contaminants that can reach the brain in utero and after birth. Excess brain exposure to either essential or non-essential elements can result in brain dyshomeostasis, which has been implicated in both neurodevelopmental disorders (NDDs; autism spectrum disorder, schizophrenia, and attention deficit hyperactivity disorder) and neurodegenerative diseases (NDGDs; Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, and amyotrophic lateral sclerosis). This review summarizes the current understanding of the extent to which the inhalational or intranasal instillation of metals reproduces in vivo the shared features of NDDs and NDGDs, including enlarged lateral ventricles, alterations in myelination, glutamatergic dysfunction, neuronal cell death, inflammation, microglial activation, oxidative stress, mitochondrial dysfunction, altered social behaviors, cognitive dysfunction, and impulsivity. Although evidence is limited to date, neuronal cell death, oxidative stress, and mitochondrial dysfunction are reproduced by numerous metals. Understanding the specific contribution of metals/trace elements to this neurotoxicity can guide the development of more realistic animal exposure models of human AP exposure and consequently lead to a more meaningful approach to mechanistic studies, potential intervention strategies, and regulatory requirements.

Source apportionment and toxicity assessment of PM2.5-bound PAHs in a typical iron-steel industry city in northeast China by PMF-ILCR

This study was designed to evaluate the occurrence and variation in concentrations, sources and cancer risk of PM_2.5-bound PAHs. Airborne PM_2.5-bound PAHs were sampled during a one-year campaign (2014-2015) in Anshan city, a typical iron and steel city in northeast China. A total of 374 PM_2.5 samples were collected. A source-oriented positive matrix factorization (PMF) model and PAH diagnostic ratios were used to investigate the potential sources of PAHs in the atmospheric environment of Anshan, and the lifetime cancer risk of the population associated with PAHs through inhalation exposure was assessed by a PMF-ILCR model. Concentrations of PM_2.5 and 16 PAHs ranged from 13.55 μg/m³ to 315.96 μg/m³ and 5.08 ng/m³ to 520.02 ng/m³, respectively. These values were higher in winter. PAH content from stationary sources and biomass combustion was higher than from other sources. Through the coefficient of divergence and localized PAH diagnostic ratio methods, we concluded that PM_2.5-bound PAHs in Anshan originated mainly from the following sources: biomass combustion, vehicle emissions, fugitive dust, coking dust and natural gas emissions. Based on the source-oriented PMF model, coal combustion, fugitive dust, vehicle emissions, coking dust, and biomass combustion were the main sources contributing to PM_2.5, accounting for 26.3%, 24.6%, 21.9%, 18.0%, and 6.3% of PM_2.5, respectively. According to the PMF-ILCR model results, ILCR risks estimated for adults and children were respectively 1.19 × 10^-5 and 8.55 × 10^-6 in winter, higher than in other seasons, and higher than the threshold value (10^-6). Together, vehicle emissions (diesel exhaust and gasoline exhaust), coal combustion and coking dust, contributed to over 86% of the cancer risk associated with PM_2.5-bound PAHs exposure in Anshan.

A Novel Robust Method for Solving CMB Receptor Model Based on Enhanced Sampling Monte Carlo Simulation

The traditional effective variance weighted least squares algorithms for solving CMB (Chemical Mass Balance) models have the following drawbacks: When there is collinearity among the sources or the number of species is less than the number of sources, then some negative value of contribution will appear in the results of the source apportionment or the algorithm does not converge to calculation. In this paper, a novel robust algorithm based on enhanced sampling Monte Carlo simulation and effective variance weighted least squares (ESMC-CMB) is proposed, which overcomes the above weaknesses. In the following practical instances for source apportionment, when nine species and nine sources, with no collinearity among them, are selected, EPA-CMB8.2 (U.S. Environmental Protection Agency-CMB8.2), NKCMB1.0 (NanKai University, China-CMB1.0) and ESMC-CMB can obtain similar results. When the source raise dust is added to the source profiles, or nine sources and eight species are selected, EPA-CMB8.2 and NKCMB1.0 cannot solve the model, but the proposed ESMC-CMB algorithm can achieve satisfactory results that fully verify the robustness and effectiveness of ESMC-CMB.

Pollution assessment of heavy metal accumulation in the farmland soils of Beijing's suburbs

In this study, we used GIS to assess heavy metal concentrations and their spatial variations in Beijing's suburban farmlands. The Beijing soil heavy metal background values (background values), the primary standard of the Chinese Environmental Quality Standard of Soil (primary standard), and the Environmental Quality Standard of Green Food Production Site (green standard) were used as the standards for assessing soil heavy metals. Results showed that the average Hg value was higher than that of the primary standard. The Ni and Pb values were lower than the corresponding background values. From the percentage area, the concentration is 100% area with As, more than 93% area with Cr, Cu, Pb and Zn, and 67% area with Cd and Hg, meeting the primary standard; over 97% area met the green standard. The calculated Nemerow index showed that less than 3% of the samples were slightly polluted, indicating good environmental quality. But the accumulated pollution of Cd and Hg was relatively high in some areas which was mainly affected by the high density of the industry. The farmland soils in Beijing suburbs were found to be suitable for farming, but a small percentage of the soils exceeded the green standard; Cd and Hg levels require special attention.

Developmental exposures to ultrafine particle air pollution reduces early testosterone levels and adult male social novelty preference: Risk for children's sex-biased neurobehavioral disorders

Epidemiological studies have reported associations of air pollution exposures with various neurodevelopmental disorders such as autism spectrum disorder (ASD), attention deficit and schizophrenia, all of which are male-biased in prevalence. Our studies of early postnatal exposure of mice to the ultrafine particle (UFP) component of air pollution, considered the most reactive component, provide support for these epidemiological associations, demonstrating male-specific or male-biased neuropathological changes and cognitive and impulsivity deficits consistent with these disorders. Since these neurodevelopmental disorders also include altered social behavior and communication, the current study examined the ability of developmental UFP exposure to reproduce these social behavior deficits and to determine whether any observed alterations reflected changes in steroid hormone concentrations. Elevated plus maze, social conditioned place preference, and social novelty preference were examined in adult mice that had been exposed to concentrated (10-20x) ambient UFPs averaging approximately 45 ug/m³ particle mass concentrations from postnatal day (PND) 4-7 and 10-13 for 4 h/day. Changes in serum testosterone (T) and corticosterone where measured at postnatal day (P)14 and approximately P120. UFP exposure decreased serum T concentrations on PND 14 and social nose-to-nose sniff rates with novel males in adulthood, suggesting social communication deficits in unfamiliar social contexts. Decreased sniff rates were not accounted for by alterations in fear-mediated behaviors and occurred without overt deficits in social preference, recognition or communication with a familiar animal or alterations in corticosterone. Adult T serum concentrations were positively correlated with nose to nose sniff rates. Collectively, these studies confirm another feature of male-biased neurodevelopmental disorders following developmental exposures to even very low levels of UFP air pollution that could be related to alterations in sex steroid programming of brain function.

An historical experiment: Los Angeles smog evolution observed by blimp

Observations of smog over the Los Angeles Basin (LAB) links high oxidant mixing ratios with poor visibility, sometimes <5 km. By the 1970s, investigators recognized that most of the aerosol affecting visibility was from gaseous oxidation products, sulfate, nitrate, and organic carbon. This led to the 1972-1973 Aerosol Characterization Experiment (ACHEX), which included observations at the ground and from aircraft. Part of ACHEX was the measurement of smog by blimp in a Lagrangian-like format. The experiment on September 6, 1973, demonstrated that a blimp could travel with the wind across the LAB, observing ozone (O₃) and precursors, and particles of different size ranges. These included condensation nuclei (CN) concentrations dominated by particles of ≤ 0.1 µm diameter and light scattering coefficient (b_sc) representing mainly particles of 0.1-2.0 µm diameter. The results indicated a pollutant variation similar to that measured at a fixed site. Ozone was produced in an air mass, reaching a maximum of ~400 ppb in the presence of nitrogen oxides (NO_x) and nonmethane hydrocarbons (NMHCs), then declined. Although the photochemistry was developing, b_sc grew with O₃ mixing ratio to a quasi-steady state at ~9-10 × 10^-4 m^-1, decreasing in value much later with decease in O₃. The light scattering coefficient was found to be positively associated with the O₃ mixing ratio, whereas CN concentrations were negatively proportional to O₃ mixing ratio. The blimp experiment was supported with aircraft vertical profiles and ground-level observations from a mobile laboratory. The blimp flight obtained combined gas and particle changes aloft that could not be obtained by ground or fixed-wing aircraft measurements alone. The experiment was partially successful in achieving a true Lagrangian characterization of smog chemistry in a constrained or defined "open" air mass.

IMPLICATIONS

The Los Angeles experiment demonstrated the use of a blimp as a platform for measurement of air pollution traveling with an air mass across an urban area. The method added unique data showing the relationship between photochemical smog chemistry and aerosol dynamics in smog. The method offers an alternative to reliance on smog chamber and modeling observations to designing air quality management strategies for reactive pollutants.

Coarse particle (PM10-2.5) source profiles for emissions from domestic cooking and industrial process in Central India

To develop coarse particle (PM_10-2.5, 2.5 to 10μm) chemical source profiles, real-world source sampling from four domestic cooking and seven industrial processing facilities were carried out in "Raipur-Bhilai" of Central India. Collected samples were analysed for 32 chemical species including 21 elements (Al, As, Ca, Cd, Co, Cr, Cu, Fe, Hg, K, Mg, Mn, Mo, Na, Ni, Pb, S, Sb, Se, V, and Zn) by atomic absorption spectrophotometry (AAS), 8 water-soluble ions (Na⁺, K⁺, Mg²⁺, Ca²⁺, Cl^-, F^-, NO₃^-, and SO₄^2-) by ion chromatography, ammonium (NH₄⁺) by spectrophotometry, and carbonaceous fractions (OC and EC) by thermal/optical transmittance. The carbonaceous fractions were most abundant fraction in household fuel and municipal solid waste combustion emissions while elemental species were more abundant in industrial emissions. Most of the elemental species were enriched in PM_2.5 (<2.5μm) size fraction as compared to the PM_10-2.5 fraction. Abundant Ca (13-28%) was found in steel-rolling mill (SRM) and cement production industry (CPI) emissions, with abundant Fe (14-32%) in ferro-manganese (FEMNI), steel production industry (SPI), and electric-arc welding emissions. High coefficients of divergence (COD) values (0.46 to 0.88) among the profiles indicate their differences. These region-specific source profiles are more relevant to source apportionment studies in India than profiles measured elsewhere.

Seasonal variations and source apportionment of atmospheric PM2.5-bound polycyclic aromatic hydrocarbons in a mixed multi-function area of Hangzhou, China

To reveal the seasonal variations and sources of PM_2.5-bound polycyclic aromatic hydrocarbons (PAHs) during haze and non-haze episodes, daily PM_2.5 samples were collected from March 2015 to February 2016 in a mixed multi-function area in Hangzhou, China. Ambient concentrations of 16 priority-controlled PAHs were determined. The sums of PM_2.5-bound PAH concentrations during the haze episodes were 4.52 ± 3.32 and 13.6 ± 6.29 ng m^-3 in warm and cold seasons, respectively, which were 1.99 and 1.49 times those during the non-haze episodes. Four PAH sources were identified using the positive matrix factorization model and conditional probability function, which were vehicular emissions (45%), heavy oil combustion (23%), coal and natural gas combustion (22%), and biomass combustion (10%). The four source concentrations of PAHs consistently showed higher levels in the cold season, compared with those in the warm season. Vehicular emissions were the most considerable sources that result in the increase of PM_2.5-bound PAH levels during the haze episodes, and heavy oil combustion played an important role in the aggravation of haze pollution. The analysis of air mass back trajectories indicated that air mass transport had an influence on the PM_2.5-bound PAH pollution, especially on the increased contributions from coal combustion and vehicular emissions in the cold season.

Multivariate statistical and lead isotopic analyses approach to identify heavy metal sources in topsoil from the industrial zone of Beijing Capital Iron and Steel Factory

Heavy metals are considered toxic to humans and ecosystems. In the present study, heavy metal concentration in soil was investigated using the single pollution index (PIi), the integrated Nemerow pollution index (PIN), and the geoaccumulation index (Igeo) to determine metal accumulation and its pollution status at the abandoned site of the Capital Iron and Steel Factory in Beijing and its surrounding area. Multivariate statistical (principal component analysis and correlation analysis), geostatistical analysis (ArcGIS tool), combined with stable Pb isotopic ratios, were applied to explore the characteristics of heavy metal pollution and the possible sources of pollutants. The results indicated that heavy metal elements show different degrees of accumulation in the study area, the observed trend of the enrichment factors, and the geoaccumulation index was Hg > Cd > Zn > Cr > Pb > Cu ≈ As > Ni. Hg, Cd, Zn, and Cr were the dominant elements that influenced soil quality in the study area. The Nemerow index method indicated that all of the heavy metals caused serious pollution except Ni. Multivariate statistical analysis indicated that Cd, Zn, Cu, and Pb show obvious correlation and have higher loads on the same principal component, suggesting that they had the same sources, which are related to industrial activities and vehicle emissions. The spatial distribution maps based on ordinary kriging showed that high concentrations of heavy metals were located in the local factory area and in the southeast-northwest part of the study region, corresponding with the predominant wind directions. Analyses of lead isotopes confirmed that Pb in the study soils is predominantly derived from three Pb sources: dust generated during steel production, coal combustion, and the natural background. Moreover, the ternary mixture model based on lead isotope analysis indicates that lead in the study soils originates mainly from anthropogenic sources, which contribute much more than the natural sources. Our study could not only reveal the overall situation of heavy metal contamination, but also identify the specific pollution sources.

Attributing Atmospheric Methane to Anthropogenic Emission Sources

Methane is a greenhouse gas, and increases in atmospheric methane concentration over the past 250 years have driven increased radiative forcing of the atmosphere. Increases in atmospheric methane concentration since 1750 account for approximately 17% of increases in radiative forcing of the atmosphere, and that percentage increases by approximately a factor of 2 if the effects of the greenhouse gases produced by the atmospheric reactions of methane are included in the assessment. Because of the role of methane emissions in radiative forcing of the atmosphere, the identification and quantification of sources of methane emissions is receiving increased scientific attention. Methane emission sources include biogenic, geogenic, and anthropogenic sources; the largest anthropogenic sources are natural gas and petroleum systems, enteric fermentation (livestock), landfills, coal mining, and manure management. While these source categories are well-known, there is significant uncertainty in the relative magnitudes of methane emissions from the various source categories. Further, the overall magnitude of methane emissions from all anthropogenic sources is actively debated, with estimates based on source sampling extrapolated to regional or national scale ("bottom-up analyses") differing from estimates that infer emissions based on ambient data ("top-down analyses") by 50% or more. To address the important problem of attribution of methane to specific sources, a variety of new analytical methods are being employed, including high time resolution and highly sensitive measurements of methane, methane isotopes, and other chemical species frequently associated with methane emissions, such as ethane. This Account describes the use of some of these emerging measurements, in both top-down and bottom-up methane emission studies. In addition, this Account describes how data from these new analytical methods can be used in conjunction with chemical mass balance (CMB) methods for source attribution. CMB methods have been developed over the past several decades to quantify sources of volatile organic compound (VOC) emissions and atmospheric particulate matter. These emerging capabilities for making measurements of methane and species coemitted with methane, rapidly, precisely, and at relatively low cost, used together with CMB methods of source attribution can lead to a better understanding of methane emission sources. Application of the CMB approach to source attribution in the Barnett Shale oil and gas production region in Texas demonstrates both the importance of extensive and simultaneous source testing in the region being analyzed and the potential of CMB method to quantify the relative strengths of methane emission sources.

Review of receptor modeling methods for source apportionment

Efforts have been made to relate measured concentrations of airborne constituents to their origins for more than 50 years. During this time interval, there have been developments in the measurement technology to gather highly time-resolved, detailed chemical compositional data. Similarly, the improvements in computers have permitted a parallel development of data analysis tools that permit the extraction of information from these data. There is now a substantial capability to provide useful insights into the sources of pollutants and their atmospheric processing that can help inform air quality management options. Efforts have been made to combine receptor and chemical transport models to provide improved apportionments. Tools are available to utilize limited numbers of known profiles with the ambient data to obtain more accurate apportionments for targeted sources. In addition, tools are in place to allow more advanced models to be fitted to the data based on conceptual models of the nature of the sources and the sampling/analytical approach. Each of the approaches has its strengths and weaknesses. However, the field as a whole suffers from a lack of measurements of source emission compositions. There has not been an active effort to develop source profiles for stationary sources for a long time, and with many significant sources built in developing countries, the lack of local profiles is a serious problem in effective source apportionment. The field is now relatively mature in terms of its methods and its ability to adapt to new measurement technologies, so that we can be assured of a high likelihood of extracting the maximal information from the collected data.

IMPLICATIONS

Efforts have been made over the past 50 years to use air quality data to estimate the influence of air pollution sources. These methods are now relatively mature and many are readily accessible through publically available software. This review examines the development of receptor models and the current state of the art in extracting source identification and apportionments from ambient air quality data.

A multivariate receptor modeling study of air-borne particulate PAHs: Regional contributions in a roadside environment

Understanding the geographic source contributions by particulate polycyclic aromatic hydrocarbons (PAHs) is important for the Korean peninsula due to its downwind location from source areas. Regional influence of particulate PAHs was previously identified using diagnostic ratios applied to mobile source dominated roadside sampling data (Kim et al., 2012b). However, no study has yet been conducted to quantify the regional source contributions. We applied a multivariate receptor modeling tool to identify and quantify the regional source contributions to particulate PAHs in Seoul. Sampling of roadside particulate PAHs was conducted in Seoul, Korea for four years between May 2005 and April 2009, and data analysis was performed with a new multivariate receptor model, Solver for Mixture Problem (SMP). The SMP model identified two sources, local mobile source and transported regional source, and quantified their source contributions. Analysis of the particulate PAHs data reveals three types of episodic periods: a high regional source contribution period with one case, a high mobile source contribution period with three cases, and a normal contribution period with eight cases. Four-year average particulate PAHs source contributions from the two sources are 4.6 ng m(-3) and 10.7 ng m(-3) for regional and mobile sources, respectively and equivalent to 30% and 70% of the total estimated contribution from each of these sources.

Evaluation of urban environment pollution based on the accumulation of macro- and trace elements in epiphytic lichens

Nitrogen, phosphorus, potassium, magnesium, zinc, nickel, copper, manganese, iron and lead accumulation properties of three epiphytic lichen species (Hypogymnia physodes (L.) Nyl., Parmelia sulcata Taylor and Xanthoria parietina (L.) Th. Fr.) were compared. An assessment of pollution of the municipal environment in Słupsk (Poland) according to macro- and trace elements was also done. Lichen samples were taken in Autumn 2013 from Betula pendula, Fraxinus excelsior, Acer platanoides, A. pseudoplatanus and Populus sp. trees. Sampling stations comprised of house development areas, green urban parks, vicinity of streets with heavy traffic and industrial enterprises. It was found that lichens represent diverse accumulation properties to pollutants according to the species. X. parietina indicated the highest bioaccumulation in relation to N, K, Mg, Zn and Fe, the thalli of H. physodes accumulated the largest amounts of Ni and Pb, while P. sulcata P and Cu. Manganese was accumulated in similar quantities by all species. Evidences acquired by the use of factor analysis proved that pollution in Słupsk municipal environment is a serious issue with three major sources domination: street dust, marine factor and residual oil combustion. The high-risk areas were detected and visualized using surface maps based on Kriging algorithm. It was seen that the highest pollution occurs in the town centre, while the smallest happened on its outskirts and in urban parks.

Environmental risk of heavy metal pollution and contamination sources using multivariate analysis in the soils of Varanasi environs, India

This study assessed soil pollution in the Varanasi environs of Uttar Pradesh in India. Assessing the concentration of potentially harmful heavy metals in the soils is imperative in order to evaluate the potential risks to human. To identify the concentration and sources of heavy metals and assess the soil environmental quality, 23 samples were collected from different locations covering dumping, road and agricultural area. The average concentrations of the heavy metals were all below the permissible limits according to soil quality guidelines except Cu (copper) and Pb (lead) in dumping and road soils. Soil heavy metal contamination was assessed on the basis of geoaccumulation index (Igeo), pollution index (PI) and integrated pollution index (IPI). The IPI of the metals ranged from 0.59 to 9.94, with the highest IPI observed in the dumping and road soils. A very significant correlation was found between Pb and Cu. The result of principal component analysis suggested that PC1 was mainly affected by the use of agrochemicals, PC2 was affected by vehicular emission and PC3 was affected by dumping waste. Meanwhile, PC4 was mainly controlled by parent material along with anthropogenic activities. Appropriate measures should be taken to minimize the heavy metal levels in soils and thus protect human health.

Source apportionment of polycyclic aromatic hydrocarbons in sediments from polluted rivers

Over the past few decades, in response to growing concerns about the impact of polycyclic aromatic hydrocarbons (PAHs) on human health, a variety of environmental forensics and geochemical techniques have emerged for studying organic pollutants. These techniques include chemical fingerprinting, receptor modeling, and compound-specific stable isotope analysis (CSIA). Chemical fingerprinting methodology involves the use of diagnostic ratios. Receptor modeling techniques include the chemical mass balance (CMB) model and multivariate statistics. Multivariate techniques include factor analysis with multiple linear regression (FA/MLR), positive matrix factorization (PMF), and UNMIX. This article reviews applications of chemical fingerprinting, receptor modeling, and CSIA; comments on their uses; and contrasts the strengths and weaknesses of each methodology.

The black carbon story: early history and new perspectives

A number of recent studies have suggested that black carbon (BC), the light-absorbing fraction of soot, is next to CO2 one of the strongest contributors to the global climate change. BC heats the air, darkens the snow and ice surfaces and could contribute to the melting of Arctic ice, snowpacks, and glaciers. Although soot is the oldest known pollutant its importance in climate modification has only been recently recognized. In this article, we trace the historical developments over about three decades that changed the view of the role of BC in the environment, from a pollutant of marginal importance to one of the main climate change agents. We also discuss some of the reasons for the initial lack of interest in BC and the subsequent rigorous research activity on the role of aerosols in climate change.

Source apportionment of PM10, organic carbon and elemental carbon at Swiss sites: an intercomparison of different approaches

In this study, the results of source apportionment of particulate matter (PM10), organic carbon (OC), and elemental carbon (EC) - as obtained through different approaches at different types of sites (urban background, urban roadside, and two rural sites in Switzerland) - are compared. The methods included in this intercomparison are positive matrix factorisation modelling (PMF, applied to chemical composition data including trace elements, inorganic ions, OC, and EC), molecular marker chemical mass balance modelling (MM-CMB), and the aethalometer model (AeM). At all sites, the agreement of the obtained source contributions was reasonable for OC, EC, and PM10. Based on an annual average, and at most of the considered sites, secondary organic carbon (SOC) is the component with the largest contribution to total OC; the most important primary source of OC is wood combustion, followed by road traffic. Secondary aerosols predominate in PM10. All considered techniques identified road traffic as the dominant source of EC, while wood combustion emissions are of minor importance for this constituent. The intercomparison of different source apportionment approaches is helpful to identify the strengths and the weaknesses of the different methods. Application of PMF has limitations when source emissions have a strong temporal correlation, or when meteorology has a strong impact on PM variability. In these cases, the use of PMF can result in mixed source profiles and consequently in the under- or overestimation of the real-world sources. The application of CMB models can be hampered by the unavailability of source profiles and the non-representativeness of the available profiles for local source emissions. This study also underlines that chemical transformations of molecular markers in the atmosphere can lead to the underestimation of contributions from primary sources, in particular during the summer period or when emission sources are far away from the receptor sites.

Improved apportionment of ambient PM constituents to sources in Tampa, FL, with pseudo-deterministic receptor model-II

In 2005, Park et al. developed a new Pseudo-Deterministic Receptor Model (PDRM) to apportion SO2 and ambient particulate matter (PM) constituents to local sources near Tampa Bay. Ambient pollutant measurements were fit to products of emission rates and dispersion factors constrained with a Gaussian Plume Model for individual sources. Although highly successful, ambient pollutant concentrations were affected by numerous contributing sources at a variety of distances and trajectories were complicated by shifting winds. In this work, we expanded the data set, modified the basic bilinear Gaussian filter equation to constrain solutions based on composition and temporal profiles of key marker species, and implemented a hierarchical approach to applying constraints in order of most-to-least stringent. To account for shifting winds and differing transport times for ground and elevated components of plumes from distant sources, a multiple-height trajectory method was implemented. These changes allowed the number of unknowns to be expanded, such that temporal profiles of the Gaussian dispersion terms could also be extracted from the data. Fits for all species were substantially improved, as was agreement with literature sources for both emission rates and source-particle compositions.

Source apportionment of sediment PAHs in the Pearl River Delta region (China) using nonnegative matrix factorization analysis with effective weighted variance solution

Considering the advantages and limitations of a single receptor model, in this study, a combined technique of nonnegative matrix factorization analysis with effective weighted variance solution (NMF-EWV) was proposed for source apportionment. Utilizing NMF, major linear independent factor loadings with nonnegative elements were extracted to identify potential pollution sources. Then, these physical reasonable factor loadings were regarded as source profiles to apportion contributions using effective weighted variance solutions. Evaluation results indicated that the NMF-EWV method reproduced the source profiles well, and got a reasonable apportionment results for the synthetic dataset. The methodology of the NMF-EWV was also applied to recognize sources and apportion the contributions of polycyclic aromatic hydrocarbons (PAHs) collected from freshwater and marine sediments in the Pearl River Delta (PRD) region which is one of the most industrialized and economically significant regions of China. Apportionment results showed that traffic tunnel made the largest contribution (46.49%) for the freshwater PAH sediments in the PRD, followed by coal residential source (29.61%), power plant (13.45%) and gasoline engine (10.45%). For the marine sediments, traffic tunnel was also apportioned as the largest source (57.61%), followed by power plant (22.86%), gasoline engine (17.71%) and coal residential source (1.82%). Traffic-related sources were the predominant reasons for PAH pollution in that region.

Concentrations and source apportionment of PM10 and associated major and trace elements in the Rhodes Island, Greece

Ambient concentrations of PM(10) and associated major and trace elements were measured over the cold and the warm season of 2007 at two sites located in the Rhodes Island (Greece), in Eastern Mediterranean, aimed at source apportionment by Chemical Mass Balance (CMB) receptor modeling. Source chemical profiles, necessary in CMB modeling, were obtained for a variety of emission sources that could possibly affect the study area, including sea spray, geological material, soot emissions from the nearby oil-fuelled thermal power plant, and other anthropogenic activities, such as vehicular traffic, residential oil combustion, wood burning, and uncontrolled open-air burning of agricultural biomass and municipal waste. Source apportionment of PM(10) and elemental components was carried out by employing an advanced CMB version, the Robotic Chemical Mass Balance model (RCMB). Vehicular emissions were found to be major PM(10) contributor accounting, on average, for 36.8% and 31.7% during the cold period, and for 40.9% and 39.2% in the warm period at the two sites, respectively. The second largest source of ambient PM(10), with minor seasonal variation, was secondary sulfates (mainly ammonium and calcium sulfates), with total average contribution around 16.5% and 18% at the two sites. Soil dust was also a remarkable source contributing around 22% in the warm period, whereas only around 10% in the cold season. Soot emitted from the thermal power plant was found to be negligible contributor to ambient PM(10) (<1%), however it appeared to appreciably contribute to the ambient V and Ni (11.3% and 5.1%, respectively) at one of the sites during the warm period, when electricity production is intensified. Trajectory analysis did not indicate any transport of Sahara dust; on the contrary, long range transport of soil dust from arid continental regions of Minor Asia and of biomass burning aerosol from the countries surrounding the Black Sea was considered possible.

Differences in metal concentration by particle size in house dust and soil

The majority of particles that adhere to hands are <63 μm in diameter yet risk assessments for soil remediation are typically based on soil samples sieved to <250 μm. The objective of our study was to determine if there is a significant difference in metal concentration by particle size in both house dust and soil. We obtained indoor dust and yard soil samples from 10 houses in Tucson, Arizona. All samples were sieved to <63 μm and 63 to <150 μm and analyzed for 30 elements via ICP-MS following nitric acid digestion. We conducted t-tests of the log-transformed data to assess for significant differences that were adjusted with a Bonferroni correction to account for multiple comparisons. In house dust, significant differences in concentration were observed for Be, Al, and Mo between particles sizes, with a higher concentration observed in the smaller particle sizes. Significant differences were also determined for Mg, Ca, Cr, Co, Cu, Ge, Zr, Ag, Ba, and Pb concentration in yard soil samples, with the higher concentration observed in the smaller particles size for each element. The results of this exploratory study indicate that current risk assessment practices for soil remediation may under estimate non-dietary ingestion exposure. This is of particular concern for young children who are more vulnerable to this exposure route due to their high hand mouthing frequencies. Additional studies with a greater number of samples and wider geographic distribution with different climates and soil types should be completed to determine the most relevant sampling practices for risk assessment.

Characterization and proinflammatory response of airborne biological particles from wastewater treatment plants

Wastewater contains a variety of microorganisms, and unit operations in the plants could release these biological components into the air environment. These airborne biological particles could have adverse health effects on plant workers and the downwind population. This study provides a first report on the concentration and characterization of the airborne biological particles in six wastewater treatment plants in Mumbai, India. The study indicates that 49% and 27% of the samples exceed, respectively, the exposure limit for airborne endotoxin and bacteria in occupational settings. Endotoxin was identified as the single most important component of the particulate matter responsible for induction of proinflammatory indicator (tumor necrosis factor-α) in in vitro assay. Identification of several clinically important bacterial species in the samples suggests that the workers at the treatment plant are exposed to opportunistic and infectious bacteria. Principal component analysis was used to identify the groups among the bacterial species which serves as the signature for transport study. Analysis also shows that the component related to spore-forming bacteria is present in all samples.

The impact of wood smoke on ambient PM2.5 in northern Rocky Mountain valley communities

During the winters of 2006/2007 and 2007/2008, PM2.5 source apportionment programs were carried out within five western Montana valley communities. Filter samples were analyzed for mass and chemical composition. Information was utilized in a Chemical Mass Balance (CMB) computer model to apportion the sources of PM2.5. Results showed that wood smoke (likely residential woodstoves) was the major source of PM2.5 in each of the communities, contributing from 56% to 77% of the measured wintertime PM2.5. Results of 14C analyses showed that between 44% and 76% of the measured PM2.5 came from a new carbon (wood smoke) source, confirming the results of the CMB modeling. In summary, the CMB model results, coupled with the 14C results, support that wood smoke is the major contributor to the overall PM2.5 mass in these rural, northern Rocky Mountain airsheds throughout the winter months.

Air pollution sources of PM(10) in Buenos Aires City

To elucidate the sources of PM(10) air pollution from the experimental information collected in a local air quality monitoring campaign we have applied two methods, effective variance and genetic algorithms, in the solution of the chemical mass balance. The comparison of these two mathematical approaches show that the identification of the possible sources and the evaluation of its contributions are quite independent of them. The role of possible different sources for major and trace elements and the significance of standardizing available data is also addressed. We also present a simple method for identifying the number of candidate sources, a key element defining the dimension of the search space.

Revealing source signatures in ambient BTEX concentrations

Management of ambient concentrations of Volatile Organic Compounds (VOCs) is essential for maintaining low ozone levels in urban areas where its formation is under a VOC-limited regime. The significant decrease in traffic-induced VOC emissions in many developed countries resulted in relatively comparable shares of traffic and non-traffic VOC emissions in urban airsheds. A key step for urban air quality management is allocating ambient VOC concentrations to their pertinent sources. This study presents an approach that can aid in identifying sources that contribute to observed BTEX concentrations in areas characterized by low BTEX concentrations, where traditional source apportionment techniques are not useful. Analysis of seasonal and diurnal variations of ambient BTEX concentrations from two monitoring stations located in distinct areas reveal the possibility to identify source categories. Specifically, the varying oxidation rates of airborne BTEX compounds are used to allocate contributions of traffic emissions and evaporative sources to observed BTEX concentrations.

Source apportionment of personal exposure of fine particulates among school communities in India

Source contribution estimates (SCE) of school community personal Respirable Particulate Matter (RPM) have been investigated. Reported relationships of personal RPM with Ambient-outdoors and indoor RPM levels have given the concept of defining the sources of personal exposure. Ambient-outdoors, indoors, soils and local road- traffic dusts were identified as main routes and principal sources of fine particulates at personal exposure levels. Fifteen subjects (05 from each of three schools) were selected from previous conducted study of interrelationships among classified atmospheric receptors in theses schools located in Bhilai-Durg, District Durg, India. Samples of RPM collected from identified receptors and sources were analyzed for selected chemical constituents and the chemical data has been utilized in preparation of source-receptor profiles. Chemical mass balance (CMB8) model has been used for source apportionment study. Major dominating source is ambient-outdoors in case of school located near to steel plant downwind. Indoors and road-traffic dusts have also played dominating role in case of school located near to National Highways. Indoor ventilation properties have played an important role in source contribution estimates.

Chemical mass balance source apportionment of PM10 and TSP in residential and industrial sites of an urban region of Kolkata, India

Daily average PM(10) (particulate matter which passes through a size selective impactor inlet with a 50% efficiency cut-off at 10 microm aerodynamic diameter), TSP (total suspended particulate matter) and their chemical species mass concentrations were measured at residential and industrial sites of an urban region of Kolkata during November 2003-November 2004. Source apportionment using chemical mass balance model revealed that the most dominant source throughout the study period at residential site was coal combustion (42%), while vehicular emission (47%) dominates at industrial site to PM(10). Paved road, field burning and wood combustion contributed 21%, 7% and 1% at residential site, while coal combustion, metal industry and soil dust contributed 34%, 1% and 1% at industrial site, respectively, to PM(10) during the study period. The contributors to TSP included coal combustion (37%), soil dust (19%), road dust (17%) and diesel combustion (15%) at residential site, while soil dust (36%), coal combustion (17%), solid waste (17%), road dust (16%) and tyre wear (7%) at industrial site. Significant seasonal variations of the particulate matters have been observed during the study period. In the monitoring sites total carbon, organic carbon and iron were found to be the marker species of road dust, while organic carbon, total carbon, chloride and sulfate have been observed as the marker species of soil dust in TSP.

Heavy metal and trace element distributions in groundwater in natural slopes and highly urbanized spaces in Mid-Levels area, Hong Kong

The lower slope of the Mid-Levels area, Hong Kong, is one the most heavily urbanized coastal areas in the world. A comprehensive groundwater heavy metal and trace element study was conducted in the Mid-Levels area aiming to investigate the impacts of urbanization on the aqueous distributions of these chemicals. Groundwater samples were collected in the upper natural slopes and the lower highly urbanized spaces in the area in different seasons, and analyzed for heavy metal and trace element contents. Compared to the results from natural slopes, groundwater samples in the developed spaces did not exhibit significant elevated levels in Zn, Cr, Cu, Cd, Pb and Fe, which are commonly found in stormwater. On the other hand, the samples were found to have elevated contents in Mn, V, Co and Mo, minor stormwater-related heavy metals, suggesting that stormwater drains may be leaking to some extent. However, the results suggested that the vadose zone could remove many of the heavy metals, protecting groundwater from being contaminated seriously. Statistical analysis suggested that a certain amount of Mn and Co was likely to be re-mobilized from natural soils due to the changes in local redox conditions, while Mn, V, Co and Mo may also be derived from steel corrosion as a result of prolonged submergence. Besides, the average B concentration in the developed spaces was about eight times higher than that in the natural slopes, indicating the presence of sewage. The mean Se concentration in the developed spaces was about 100 times higher than that in the natural slopes. About 40% of samples in the developed spaces contained Se level higher than the drinking water guideline value proposed by the World Health Organization. Se was found to be positively correlated with B and SO4(2-) (R = 0.534 and 0.631, respectively), suggesting that Se may also be related to leakage from sewage pipes. Part of the Sr may come from leakage of flushing water and/or sewage as Sr was strongly correlated with Cl- (R = 0.929). According to the measured results, deep groundwater samples collected from piezometers (> 10 m in depth) in the urbanized spaces appeared to be virtually free from any anthropogenic contaminations. This study may shed important light on the identification and evaluation of leakage from service pipes in a particular area based on aqueous distributions of heavy metals and trace elements. Moreover, the above findings may be instructional for other coastal cities with a similar level of urban development to understand the potential threats to their groundwater resources.

Residential biofuels in South Asia: carbonaceous aerosol emissions and climate impacts

High concentrations of pollution particles, including "soot" or black carbon, exist over the Indian Ocean, but their sources and geographical origins are not well understood. We measured emissions from the combustion of biofuels, used widely in south Asia for cooking, and found that large amounts of carbonaceous aerosols are emitted per kilogram of fuel burnt. We calculate that biofuel combustion is the largest source of black carbon emissions in India, and we suggest that its control is central to climate change mitigation in the south Asian region.

Daily concentrations of trace metals in aerosols in Beijing, China, determined by using inductively coupled plasma mass spectrometry equipped with laser ablation analysis, and source identification of aerosols

This paper describes the daily concentrations of trace metals and ionic constituents in the aerosol of Beijing, China from March 2001 to August 2003. Daily PM10 concentrations were also measured from September 2001 to August 2003. The daily average PM10 concentration at Beijing, China from September 2001 to August 2003 was 171+/-117 microg m(-3) (n = 673), which is 5-fold higher than at Yokohama, Japan. Trace metal concentrations were analyzed by using inductively coupled plasma mass spectrometry equipped with a laser ablation sample introduction (LA/ICP-MS), which is a rapid and simultaneous method for multi-element analysis. The daily average metal concentrations in TSP in Beijing from March 2001 to August 2003 were: Al: 3.5+/-2.4 (n = 727), Ti: 0.47+/-0.35 (n = 720), V: 0.013+/-0.010 (n = 716), Cr: 0.019+/-0.015 (n = 618), Mn: 0.24+/-0.16 (n = 730), Fe: 5.5+/-3.9 (n = 728), Co: 0.0046+/-0.0055 (n = 629), Ni: 0.022+/-0.024 (n = 680), Cu: 0.11+/-0.11 (n = 660), Zn: 0.77+/-0.60 (n = 726), As: 0.048+/-0.047 (n = 731), Se: 0.010+/-0.010 (n = 550), Cd: 0.0068+/-0.0082 (n = 709), Sb: 0.033+/-0.036 (n = 687), and Pb: 0.43+/-0.50 (n = 728) (unit, microg m(-3)). All the metal concentrations in TSP in Beijing, China were 1.7-21.8 times higher than those in TSP in the center of Tokyo, Japan. Notably, As concentrations in TSP in Beijing were 20-fold higher than those in Tokyo. Source identification of aerosols in Beijing was carried out by using the chemical mass balance (CMB) receptor model, with the daily concentration of metals in the aerosol. The major primary sources of the aerosol of Beijing were considered to be soil dust and coal combustion. Vehicle exhaust contribution tended to increase.

Determination of multi-element profiles of street dust using energy dispersive X-ray fluorescence (EDXRF)

Street dust samples have been collected in different areas in Hong Kong associated with various levels of traffic and pedestrian flow, and the concentrations of 23 chemical elements have been determined using energy dispersive X-ray fluorescence (EDXRF). The 23 studied elements were Na, Al, Si, Cl, Ti, Ba, V, Cr, Mn, Fe, Co, K, Ca, Ni, Cu, Zn, As, Pb, Rb, Sr, Y, Zr and Sn. A profile for average street dust for Hong Kong has been determined by taking average values for different areas. The values for the Hong Kong street dust are commensurate with the values derived in previous investigations or for other countries, except that Hong Kong street dust has much higher Cl, Ca and As concentrations. A factor analysis gives four sources for the street dust in Hong Kong: namely, mixture of metallic dust and crustal material, vehicles, road pavement materials, and mixture of marine aerosols and crustal material.

Receptor modeling application framework for particle source apportionment

Receptor models infer contributions from particulate matter (PM) source types using multivariate measurements of particle chemical and physical properties. Receptor models complement source models that estimate concentrations from emissions inventories and transport meteorology. Enrichment factor, chemical mass balance, multiple linear regression, eigenvector. edge detection, neural network, aerosol evolution, and aerosol equilibrium models have all been used to solve particulate air quality problems, and more than 500 citations of their theory and application document these uses. While elements, ions, and carbons were often used to apportion TSP, PM10, and PM2.5 among many source types, many of these components have been reduced in source emissions such that more complex measurements of carbon fractions, specific organic compounds, single particle characteristics, and isotopic abundances now need to be measured in source and receptor samples. Compliance monitoring networks are not usually designed to obtain data for the observables, locations, and time periods that allow receptor models to be applied. Measurements from existing networks can be used to form conceptual models that allow the needed monitoring network to be optimized. The framework for using receptor models to solve air quality problems consists of: (1) formulating a conceptual model; (2) identifying potential sources; (3) characterizing source emissions; (4) obtaining and analyzing ambient PM samples for major components and source markers; (5) confirming source types with multivariate receptor models; (6) quantifying source contributions with the chemical mass balance; (7) estimating profile changes and the limiting precursor gases for secondary aerosols; and (8) reconciling receptor modeling results with source models, emissions inventories, and receptor data analyses.

A pilot study to assess ground-level ambient air concentrations of fine particles and carbon monoxide in urban Guatemala

Ambient concentrations and the elemental composition of particles less than 2.5 microm in diameter (PM2.5), as well as carbon monoxide (CO) concentrations, were measured at ground-level in three Guatemalan cities in summer 1997: Guatemala City, Quetzaltenango, and Antigua. This pilot study also included quantitative and qualitative characterizations of microenvironment conditions, e.g., local meteorology, reported elsewhere. The nondestructive X-ray fluorescence elemental analysis (XRF) of Teflon filters was conducted. The highest integrated average PM2.5. concentrations in an area (zona) of Guatemala City and Quetzaltenango were 150 microg m(-3) (zona 12) and 120 microg m(-3) (zona 2), respectively. The reported integrated average PM2.5 concentration for Antigua was 5 microg m(-3). The highest observed half-hour and monitoring period average CO concentrations in Guatemala City were 10.9 ppm (zona 8) and 7.2 ppm (zonas 8 and 10), respectively. The average monitoring period CO concentration in Antigua was 2.6 ppm. Lead and bromine concentrations were negligible, indicative of the transition to unleaded fuel use in cars and motorcycles. The XRF results suggested sources of air pollution in Guatemala, where relative rankings varied by city and by zonas within each city, were fossil fuel combustion emitting hydrocarbons, combustion of sulfurous conventional fuels, soil/roadway dust, farm/agricultural dust, and vehicles (evaportion of gas, parts' wear).