Chemical Characterization and Source Apportionment of Household Fine Particulate Matter in Rural, Peri-urban, and Urban West Africa

Household air pollution in sub-Saharan Africa and other developing regions is an important cause of disease burden. Little is known about the chemical composition and sources of household air pollution in sub-Saharan Africa, and how they differ between rural and urban homes. We analyzed the chemical composition and sources of fine particles (PM2.5) in household cooking areas of multiple neighborhoods in Accra, Ghana, and in peri-urban (Banjul) and rural (Basse) areas in The Gambia. In Accra, biomass burning accounted for 39–62% of total PM2.5 mass in the cooking area in different neighborhoods; the absolute contributions were 10–45 μg/m3. Road dust and vehicle emissions comprised 12–33% of PM2.5 mass. Solid waste burning was also a significant contributor to household PM2.5 in a low-income neighborhood but not for those living in better-off areas. In Banjul and Basse, biomass burning was the single dominant source of cooking-area PM2.5, accounting for 74–87% of its total mass; the relative and absolute contributions of biomass smoke to PM2.5 mass were larger in households that used firewood than in those using charcoal, reaching as high as 463 μg/m3 in Basse homes that used firewood for cooking. Our findings demonstrate the need for policies that enhance access to cleaner fuels in both rural and urban areas, and for controlling traffic emissions in cities in sub-Saharan Africa

Evaluation of the performance of four chemical transport models in predicting the aerosol chemical composition in Europe in 2005

© Author(s) 2016.Four regional chemistry transport models were applied to simulate the concentration and composition of particulate matter (PM) in Europe for 2005 with horizontal resolution 20 km. The modelled concentrations were compared with the measurements of PM chemical composition by the European Monitoring and Evaluation Programme (EMEP) monitoring network. All models systematically underestimated PM10 and PM2:5 by 10–60 %, depending on the model and the season of the year, when the calculated dry PM mass was compared with the measurements. The average water content at laboratory conditions was estimated between 5 and 20% for PM2:5 and between 10 and 25% for PM10. For majority of the PM chemical components, the relative underestimation was smaller than it was for total PM, exceptions being the carbonaceous particles and mineral dust. Some species, such as sea salt and NO3, were overpredicted by the models. There were notable differences between the models’ predictions of the seasonal variations of PM, mainly attributable to different treatments or omission of some source categories and aerosol processes. Benzo(a)pyrene concentrations were overestimated by all the models over the whole year. The study stresses the importance of improving the models’ skill in simulating mineral dust and carbonaceous compounds, necessity for high-quality emissions from wildland fires, as well as the need for an explicit consideration of aerosol water content in model–measurement comparison.Peer reviewedFinal Published versio

SPARTAN: a global network to evaluate and enhance satellite-based estimates of ground-level particulate matter for global health applications

Ground-based observations have insufficient spatial coverage to assess long-term human exposure to fine particulate matter (PM2.5) at the global scale. Satellite remote sensing offers a promising approach to provide information on both short-and long-term exposure to PM2.5 at local-to-global scales, but there are limitations and outstanding questions about the accuracy and precision with which ground-level aerosol mass concentrations can be inferred from satellite remote sensing alone. A key source of uncertainty is the global distribution of the relationship between annual average PM2.5 and discontinuous satellite observations of columnar aerosol optical depth (AOD). We have initiated a global network of ground-level monitoring stations designed to evaluate and enhance satellite remote sensing estimates for application in health-effects research and risk assessment. This Surface PARTiculate mAtter Network (SPARTAN) includes a global federation of ground-level monitors of hourly PM2.5 situated primarily in highly populated regions and collocated with existing ground-based sun photometers that measure AOD. The instruments, a three-wavelength nephelometer and impaction filter sampler for both PM2.5 and PM10, are highly autonomous. Hourly PM2.5 concentrations are inferred from the combination of weighed filters and nephelometer data. Data from existing networks were used to develop and evaluate network sampling characteristics. SPARTAN filters are analyzed for mass, black carbon, water-soluble ions, and metals. These measurements provide, in a variety of regions around the world, the key data required to evaluate and enhance satellite-based PM2.5 estimates used for assessing the health effects of aerosols. Mean PM2.5 concentrations across sites vary by more than 1 order of magnitude. Our initial measurements indicate that the ratio of AOD to ground-level PM2.5 is driven temporally and spatially by the vertical profile in aerosol scattering. Spatially this ratio is also strongly influenced by the mass scattering efficiency.Fil: Snider, G.. Dalhousie University Halifax; CanadáFil: Weagle, C. L.. Dalhousie University Halifax; CanadáFil: Martin, R. V.. Dalhousie University Halifax; Canadá. University of Cambridge; Reino UnidoFil: van Donkelaar, A.. Dalhousie University Halifax; CanadáFil: Conrad, K.. Dalhousie University Halifax; CanadáFil: Cunningham, D.. Dalhousie University Halifax; CanadáFil: Gordon, C.. Dalhousie University Halifax; CanadáFil: Zwicker, M.. Dalhousie University Halifax; CanadáFil: Akoshile, C.. University of Ilorin; NigeriaFil: Artaxo, P.. Governo Do Estado de Sao Paulo; BrasilFil: Anh, N. X.. Vietnam Academy of Science and Technology. Institute of Geophysics; VietnamFil: Brook, J.. University of Toronto; CanadáFil: Dong, J.. Tsinghua University; ChinaFil: Garland, R. M.. North-West University; SudáfricaFil: Greenwald, R.. Rollins School of Public Health; Estados UnidosFil: Griffith, D.. Council for Scientific and Industrial Research; SudáfricaFil: He, K.. Tsinghua University; ChinaFil: Holben, B. N.. NASA Goddard Space Flight Center; Estados UnidosFil: Kahn, R.. NASA Goddard Space Flight Center; Estados UnidosFil: Koren, I.. Weizmann Institute Of Science Israel; IsraelFil: Lagrosas, N.. Manila Observatory, Ateneo de Manila University campus; FilipinasFil: Lestari, P.. Institut Teknologi Bandung; IndonesiaFil: Ma, Z.. Rollins School of Public Health; Estados UnidosFil: Vanderlei Martins, J.. University of Maryland; Estados UnidosFil: Quel, Eduardo Jaime. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rudich, Y.. Weizmann Institute Of Science Israel; IsraelFil: Salam, A.. University Of Dhaka; BangladeshFil: Tripathi, S. N.. Indian Institute Of Technology, Kanpur; IndiaFil: Yu, C.. Rollins School of Public Health; Estados UnidosFil: Zhang, Q.. Tsinghua University; ChinaFil: Zhang, Y.. Tsinghua University; ChinaFil: Brauer, M.. University of British Columbia; CanadáFil: Cohen, A.. Health Effects Institute; Estados UnidosFil: Gibson, M. D.. Dalhousie University Halifax; CanadáFil: Liu, Y.. Rollins School of Public Health; Estados Unido

Ensemble learning of model hyperparameters and spatiotemporal data for calibration of low-cost PM2.5 sensors.

he PM2.5 air quality index (AQI) measurements from government-built supersites are accurate but cannot provide a dense coverage of monitoring areas. Low-cost PM2.5 sensors can be used to deploy a fine-grained internet-of-things (IoT) as a complement to government facilities. Calibration of low-cost sensors by reference to high-accuracy supersites is thus essential. Moreover, the imputation for missing-value in training data may affect the calibration result, the best performance of calibration model requires hyperparameter optimization, and the affecting factors of PM2.5 concentrations such as climate, geographical landscapes and anthropogenic activities are uncertain in spatial and temporal dimensions. In this paper, an ensemble learning for imputation method selection, calibration model hyperparameterization, and spatiotemporal training data composition is proposed. Three government supersites are chosen in central Taiwan for the deployment of low-cost sensors and hourly PM2.5 measurements are collected for 60 days for conducting experiments. Three optimizers, Sobol sequence, Nelder and Meads, and particle swarm optimization (PSO), are compared for evaluating their performances with various versions of ensembles. The best calibration results are obtained by using PSO, and the improvement ratios with respect to R2, RMSE, and NME, are 4.92%, 52.96%, and 56.85%, respectively

Longitudinal survey of microbiome associated with particulate matter in a megacity.

BackgroundWhile the physical and chemical properties of airborne particulate matter (PM) have been extensively studied, their associated microbiome remains largely unexplored. Here, we performed a longitudinal metagenomic survey of 106 samples of airborne PM2.5 and PM10 in Beijing over a period of 6 months in 2012 and 2013, including those from several historically severe smog events.ResultsWe observed that the microbiome composition and functional potential were conserved between PM2.5 and PM10, although considerable temporal variations existed. Among the airborne microorganisms, Propionibacterium acnes, Escherichia coli, Acinetobacter lwoffii, Lactobacillus amylovorus, and Lactobacillus reuteri dominated, along with several viral species. We further identified an extensive repertoire of genes involved in antibiotic resistance and detoxification, including transporters, transpeptidases, and thioredoxins. Sample stratification based on Air Quality Index (AQI) demonstrated that many microbial species, including those associated with human, dog, and mouse feces, exhibit AQI-dependent incidence dynamics. The phylogenetic and functional diversity of air microbiome is comparable to those of soil and water environments, as its composition likely derives from a wide variety of sources.ConclusionsAirborne particulate matter accommodates rich and dynamic microbial communities, including a range of microbial elements that are associated with potential health consequences

Characterization of lead-recycling facility emissions at various workplaces: Major insights for sanitary risks assessment

Most available studies on lead smelter emissions deal with the environmental impact of outdoor particles, but only a few focus on air quality at workplaces. The objective of this study is to physically and chemically characterize the Pb-rich particles emitted at different workplaces in a lead recycling plant. A multiscale characterization was conducted from bulk analysis to the level of individual particles, to assess the particles properties in relation with Pb speciation and availability. Process PM from various origins were sampled and then compared; namely Furnace and Refining PM respectively present in the smelter and at refinery workplaces, Emissions PM present in channeled emissions. These particles first differed by their morphology and size distribution, with finer particles found in emissions. Differences observed in chemical composition could be explained by the industrial processes. All PM contained the same major phases (Pb, PbS, PbO, PbSO4 and PbO·PbSO4) but differed on the nature and amount of minor phases. Due to high content in PM, Pb concentrations in the CaCl2 extractant reached relatively high values (40mgL−1). However, the ratios (soluble/total) of CaCl2 exchangeable Pb were relatively low (<0.02%) in comparison with Cd (up to 18%). These results highlight the interest to assess the soluble fractions of all metals (minor and major) and discuss both total metal concentrations and ratios for risk evaluations. In most cases metal extractability increased with decreasing size of particles, in particular, lead exchangeability was highest for channeled emissions. Such type of study could help in the choice of targeted sanitary protection procedures and for further toxicological investigations. In the present context, particular attention is given to Emissions and Furnace PM. Moreover, exposure to other metals than Pb should be considered

Haze in the Klang Valley of Malaysia

Continuous measurements of dry aerosol light scattering (Bsp) were made at two sites in the Klang Valley of Malaysia between December 1998 and December 2000. In addition 24-h PM2.5 samples were collected on a one-day-in-six cycle and the chemical composition of the aerosol was determined. Periods of excessive haze were defined as 24-h average Bsp values greater than 150 Mm-1 and these occurred on a number of occasions, between May and September 1999, during May 2000, and between July and September 2000. The evidence for smoke being a significant contributor to aerosol during periods of excessive haze is discussed and includes features of the aerosol chemistry, the diurnal cycle of Bsp, and the coincidence of forest fires on Sumatra during the southwest (SW) monsoon period, as well as transport modelling for one week of the southwest Monsoon of 2000. The study highlights that whilst transboundary smoke is a major contributor to poor visibility in the Klang Valley, smoke from fires on Peninsular Malaysia is also a contributor, and at all times, the domestic source of secondary particle production is present