Effects of long-range transported air pollution from vegetation fires on daily mortality and hospital admissions in the Helsinki metropolitan area, Finland

Introduction: Fine particulate matter (PM2.5) emissions from vegetation fires can be transported over long distances and may cause significant air pollution episodes far from the fires. However, epidemiological evidence on health effects of vegetation-fire originated air pollution is limited, particularly for mortality and cardiovascular outcomes. Objective: We examined association between short-term exposure to long-range transported PM2.5 from vegetation fires and daily mortality due to non-accidental, cardiovascular, and respiratory causes and daily hospital admissions due to cardiovascular and respiratory causes in the Helsinki metropolitan area, Finland. Methods: Days significantly affected by smoke from vegetation fires between 2001 and 2010 were identified using air quality measurements at an urban background and a regional background monitoring station, and modelled data on surface concentrations of vegetation-fire smoke. Associations between daily PM2.5 concentration and health outcomes on i) smoke-affected days and ii) all other days (i.e. non smoke days) were analysed using Poisson time series regression. All statistical models were adjusted for daily temperature and relative humidity, influenza, pollen, and public holidays. Results: On smoke-affected days, 10 mu g/m(3) increase in PM2.5 was associated with a borderline statistically significant increase in cardiovascular mortality among total population at a lag of three days (12.4%, 95% CI -0.2% to 26.5%), and among the elderly (>= 65 years) following same-day exposure (13.8%, 95% CI -0.6% to 30.4%) and at a lag of three days (11.8%, 95% CI -2.2% to 27.7%). Smoke day PM2.5 was not associated with non-accidental mortality or hospital admissions due to cardiovascular causes. However, there was an indication of a positive association with hospital admissions due to respiratory causes among the elderly, and admissions due to chronic obstructive pulmonary disease or asthma among the total population. In contrast, on non-smoke days PM2.5 was generally not associated with the health outcomes, apart from suggestive small positive effects on non-accidental mortality at a lag of one day among the elderly and hospital admissions due to all respiratory causes following same-day exposure among the total population. Conclusions: Our research provides suggestive evidence for an association of exposure to long-range transported PM2.5 from vegetation fires with increased cardiovascular mortality, and to a lesser extent with increased hospital admissions due to respiratory causes. Hence, vegetation-fire originated air pollution may have adverse effects on public health over a distance of hundreds to thousands of kilometres from the fires. (C) 2016 The Authors. Published by Elsevier Inc.Peer reviewe

Evaluation of the impact of wood combustion on benzo[a] pyrene (BaP) concentrations; ambient measurements and dispersion modeling in Helsinki, Finland

Even though emission inventories indicate that wood combustion is a major source of polycyclic aromatic hydrocarbons (PAHs), estimating its impacts on PAH concentration in ambient air remains challenging. In this study the effect of local small-scale wood combustion on the benzo[a] pyrene (BaP) concentrations in ambient air in the Helsinki metropolitan area in Finland is evaluated, using ambient air measurements, emission estimates, and dispersion modeling. The measurements were conducted at 12 different locations during the period from 2007 to 2015. The spatial distributions of annual average BaP concentrations originating from wood combustion were predicted for four of those years: 2008, 2011, 2013, and 2014. According to both the measurements and the dispersion modeling, the European Union target value for the annual average BaP concentrations (1 ngm(-3) ) was clearly exceeded in certain suburban detached-house areas. However, in most of the other urban areas, including the center of Helsinki, the concentrations were below the target value. The measured BaP concentrations highly correlated with the measured levoglucosan concentrations in the suburban detached-house areas. In street canyons, the measured concentrations of BaP were at the same level as those in the urban background, clearly lower than those in suburban detached-house areas. The predicted annual average concentrations matched with the measured concentrations fairly well. Both the measurements and the modeling clearly indicated that wood combustion was the main local source of ambient air BaP in the Helsinki metropolitan area.Peer reviewe

Changes in background aerosol composition in Finland during polluted and clean periods studied by TEM/EDX individual particle analysis

Aerosol samples were collected at a rural background site in southern Finland in May 2004 during pollution episode ( PM1 similar to 16 mu g m(-3), backward air mass trajectories from south-east), intermediate period (PM1 similar to 5 mu g m(-3), backtrajectories from north-east) and clean period (PM1 similar to 2 mu g m(-3), backtrajectories from northwest/ north). The elemental composition, morphology and mixing state of individual aerosol particles in three size fractions were studied using transmission electron microscopy (TEM) coupled with energy dispersive X-ray (EDX) microanalyses. The TEM/EDX results were complemented with the size-segregated bulk chemical measurements of selected ions and organic and elemental carbon. Many of the particles in PM0.2-1 and PM1-3.3 size fractions were strongly internally mixed with S, C and/or N. The major particle types in PM0.2-1 samples were 1) soot and 2) ( ammonium) sulphates and their mixtures with variable amounts of C, K, soot and/or other inclusions. Number proportions of those two particle groups in PM0.2-1 samples were 0 - 12% and 83 - 97%, respectively. During the pollution episode, the proportion of Ca-rich particles was very high ( 26 - 48%) in the PM1- 3.3 and PM3.3-11 samples, while the PM0.2-1 and PM1- 3.3 samples contained elevated proportions of silicates ( 22 - 33%), metal oxides/hydroxides ( 1 - 9%) and tar balls ( 1 - 4%). These aerosols originated mainly from polluted areas of Eastern Europe, and some open biomass burning smoke was also brought by long-range transport. During the clean period, when air masses arrived from the Arctic Ocean, PM1- 3.3 samples contained mainly sea salt particles ( 67 - 89%) with a variable rate of Cl substitution ( mainly by NO3-). During the intermediate period, the PM1- 3.3 sample contained porous (sponge-like) Na-rich particles (35%) with abundant S, K and O. They might originate from the burning of wood pulp wastes of paper industry. The proportion of biological particles and C-rich fragments ( probably also biological origin) were highest in the PM3.3-11 samples ( 0 - 81% and 0 - 22%, respectively). The origin of different particle types and the effect of aging processes on particle composition and their hygroscopic and optical properties are discussed.Aerosol samples were collected at a rural background site in southern Finland in May 2004 during pollution episode ( PM1 similar to 16 mu g m(-3), backward air mass trajectories from south-east), intermediate period (PM1 similar to 5 mu g m(-3), backtrajectories from north-east) and clean period (PM1 similar to 2 mu g m(-3), backtrajectories from northwest/ north). The elemental composition, morphology and mixing state of individual aerosol particles in three size fractions were studied using transmission electron microscopy (TEM) coupled with energy dispersive X-ray (EDX) microanalyses. The TEM/EDX results were complemented with the size-segregated bulk chemical measurements of selected ions and organic and elemental carbon. Many of the particles in PM0.2-1 and PM1-3.3 size fractions were strongly internally mixed with S, C and/or N. The major particle types in PM0.2-1 samples were 1) soot and 2) ( ammonium) sulphates and their mixtures with variable amounts of C, K, soot and/or other inclusions. Number proportions of those two particle groups in PM0.2-1 samples were 0 - 12% and 83 - 97%, respectively. During the pollution episode, the proportion of Ca-rich particles was very high ( 26 - 48%) in the PM1- 3.3 and PM3.3-11 samples, while the PM0.2-1 and PM1- 3.3 samples contained elevated proportions of silicates ( 22 - 33%), metal oxides/hydroxides ( 1 - 9%) and tar balls ( 1 - 4%). These aerosols originated mainly from polluted areas of Eastern Europe, and some open biomass burning smoke was also brought by long-range transport. During the clean period, when air masses arrived from the Arctic Ocean, PM1- 3.3 samples contained mainly sea salt particles ( 67 - 89%) with a variable rate of Cl substitution ( mainly by NO3-). During the intermediate period, the PM1- 3.3 sample contained porous (sponge-like) Na-rich particles (35%) with abundant S, K and O. They might originate from the burning of wood pulp wastes of paper industry. The proportion of biological particles and C-rich fragments ( probably also biological origin) were highest in the PM3.3-11 samples ( 0 - 81% and 0 - 22%, respectively). The origin of different particle types and the effect of aging processes on particle composition and their hygroscopic and optical properties are discussed.Aerosol samples were collected at a rural background site in southern Finland in May 2004 during pollution episode ( PM1 similar to 16 mu g m(-3), backward air mass trajectories from south-east), intermediate period (PM1 similar to 5 mu g m(-3), backtrajectories from north-east) and clean period (PM1 similar to 2 mu g m(-3), backtrajectories from northwest/ north). The elemental composition, morphology and mixing state of individual aerosol particles in three size fractions were studied using transmission electron microscopy (TEM) coupled with energy dispersive X-ray (EDX) microanalyses. The TEM/EDX results were complemented with the size-segregated bulk chemical measurements of selected ions and organic and elemental carbon. Many of the particles in PM0.2-1 and PM1-3.3 size fractions were strongly internally mixed with S, C and/or N. The major particle types in PM0.2-1 samples were 1) soot and 2) ( ammonium) sulphates and their mixtures with variable amounts of C, K, soot and/or other inclusions. Number proportions of those two particle groups in PM0.2-1 samples were 0 - 12% and 83 - 97%, respectively. During the pollution episode, the proportion of Ca-rich particles was very high ( 26 - 48%) in the PM1- 3.3 and PM3.3-11 samples, while the PM0.2-1 and PM1- 3.3 samples contained elevated proportions of silicates ( 22 - 33%), metal oxides/hydroxides ( 1 - 9%) and tar balls ( 1 - 4%). These aerosols originated mainly from polluted areas of Eastern Europe, and some open biomass burning smoke was also brought by long-range transport. During the clean period, when air masses arrived from the Arctic Ocean, PM1- 3.3 samples contained mainly sea salt particles ( 67 - 89%) with a variable rate of Cl substitution ( mainly by NO3-). During the intermediate period, the PM1- 3.3 sample contained porous (sponge-like) Na-rich particles (35%) with abundant S, K and O. They might originate from the burning of wood pulp wastes of paper industry. The proportion of biological particles and C-rich fragments ( probably also biological origin) were highest in the PM3.3-11 samples ( 0 - 81% and 0 - 22%, respectively). The origin of different particle types and the effect of aging processes on particle composition and their hygroscopic and optical properties are discussed.Peer reviewe

Determinants of spatial variability of air pollutant concentrations in a street canyon network measured using a mobile laboratory and a drone

Urban air pollutant concentrations are highly variable both in space and time. In order to understand these variabilities high-resolution measurements of air pollutants are needed. Here we present results of a mobile laboratory and a drone measurements made within a street-canyon network in Helsinki, Finland, in summer and winter 2017. The mobile lab-oratory measured the total number concentration (N) and lung-deposited surface area (LDSA) of aerosol particles, and the concentrations of black carbon, nitric oxide (NOx) and ozone (O3). The drone measured the vertical profile of LDSA. The main aims were to examine the spatial variability of air pollutants in a wide street canyon and its immediate surroundings, and find the controlling environmental variables for the observed variability's.The highest concentrations with the most temporal variability were measured at the main street canyon when the mo-bile laboratory was moving with the traffic fleet for all air pollutants except O3. The street canyon concentration levels were more affected by traffic rates whereas on surrounding areas, meteorological conditions dominated. Both the mean flow and turbulence were important, the latter particularly for smaller aerosol particles through LDSA and N. The formation of concentration hotspots in the street network were mostly controlled by mechanical processes but in winter thermal processes became also important for aerosol particles. LDSA showed large variability in the profile shape, and surface and background concentrations. The expected exponential decay functions worked better in well -mixed conditions in summer compared to winter. We derived equation for the vertical decay which was mostly con-trolled by the air temperature. Mean wind dominated the profile shape over both thermal and mechanical turbulence. This study is among the first experimental studies to demonstrate the importance of high-resolution measurements in understanding urban pollutant variability in detail.Peer reviewe

Concentration variation of gaseous and particulate pollutants in the helsinki city centre — observations from a two-year campaign from 2013–2015

The main chemical composition of PM1 (total organics, black carbon, sulphate, nitrate and ammonium), mass concentrations of PM2.5 and PM2.5–10 and concentration of specific trace gases were measured in a high temporal resolution from May 2013 to April 2015 in the city centre of Helsinki, Finland. On average, the concentrations of PM2.5 and PM2.5–10 were 9.1 µg m–3 and 16 µg m–3, respectively, during a two-year campaign. PM1 consisted mostly of organics (60%), followed by sulphate (12%), black carbon (11%), nitrate (9.8%) and ammonium (6.5%). The particle and gas data were combined with the meteorological data in order to obtain information on how local meteorology affects concentrations of air pollutants. Two meteorological parameters that mostly affected the pollutant concentrations were the wind speed and temperature, while sulphate and PM2.5–10 were also impacted by the relative humidity. The highest concentrations of the measured PM1 components were observed when the wind was calm or the temperature was either very cold or very warm. PM2.5–10 concentrations were at the highest during calm or very windy conditions, due to local street and construction dust. The seasonal-and diurnal-varying mixing height did not seem to affect markedly the concentrations of pollutants. Overall, air quality in terms of the aerosol mass was governed by three different main pollution sources in the Helsinki city centre: 1) local sources, of which traffic-related emissions were the most important; 2) long-range or regional transport of pollutants; and 3) local sources of organic aerosol. © 2019, Finnish Environment Institute. All rights reserved.Peer reviewe

Using monosaccharide anhydrides to estimate the impact of wood combustion on fine particles in the Helsinki Metropolitan Area

Peer reviewe

Intelligent Calibration and Virtual Sensing for Integrated Low-Cost Air Quality Sensors

This paper presents the development of air quality low-cost sensors (LCS) with improved accuracy features. The LCS features integrate machine learning based calibration models and virtual sensors. LCS performances are analyzed and some LCS variables with low performance are improved through intelligent field-calibrations. Meteorological variables are calibrated using linear dynamic models. While, due to the non-linear relationship to reference instruments, fine particulate matter (PM2.5) are calibrated using non-linear machine learning models. However, due to sensor drifts or faults, carbon dioxide (CO2) does not present correlation to reference instrument. As a result, the LCS for CO2 is not feasible to be calibrated. Hence, to estimate the CO2 concentration, mathematical models are developed to be integrated in the calibrated LCS, known as a virtual sensor. In addition, another virtual sensor is developed to demonstrate the capability of estimating air pollutant concentrations, e.g. black carbon, when the physical sensor devices are not available. In our paper, calibration models and virtual sensors are established using corresponding reference instruments that are installed on two reference stations. This strategy generalizes the models of calibration and virtual sensing which then allows LCS to be deployed in field independently with a high accuracy. Our proposed methodology enables scaling-up accurate air pollution mapping appropriate for smart cities.Peer reviewe

Air Pollution Exposure Monitoring using Portable Low-cost Air Quality Sensors

Urban environments with a high degree of industrialization are infested with hazardous chemicals and airborne pollutants. These pollutants can have devastating effects on human health, causing both acute and chronic diseases such as respiratory infections, lung cancer, and heart disease. Air pollution monitoring is vital not only to citizens, warning them on the health risks of air pollutants, but also to policy-makers,assisting them on drafting regulations and laws that aim at minimizing those health risks. Currently,air pollution monitoring predominantly relies on expensive high-end static sensor stations. These stations produce only aggregated information about air pollutants, and are unable to capture variations in individual’s air pollution exposure. As an alternative, this article develops a citizen-based air pollution monitoring system that captures individual exposure levels to air pollutants during daily indoor and outdoor activities. We present a low-cost portable sensor and carry out a measurement campaign using the sensors to demonstrate the validity and benefits of citizen-based pollution measurements. Specifically, we (i) successfully classify the data into indoor and outdoor, and (ii) validate the consistency and accuracy of our outdoor-classified data to the measurements of a high-end reference monitoring station. Our experimental results further prove the effectiveness of our campaign by (i) providing fine-grained air pollution insights over a wide geographical area, (ii) identifying probable causes of air pollution dependent on the area, and (iii) providing citizens with personalized insights about air pollutants in their daily commute.Peer reviewe

Input-adaptive linear mixed-effects model for estimating alveolar lung-deposited surface area (LDSA) using multipollutant datasets

Lung-deposited surface area (LDSA) has been considered to be a better metric to explain nanoparticle toxicity instead of the commonly used particulate mass concentration. LDSA concentrations can be obtained either by direct measurements or by calculation based on the empirical lung deposition model and measurements of particle size distribution. However, the LDSA or size distribution measurements are neither compulsory nor regulated by the government. As a result, LDSA data are often scarce spatially and temporally. In light of this, we developed a novel statistical model, named the input-adaptive mixed-effects (IAME) model, to estimate LDSA based on other already existing measurements of air pollutant variables and meteorological conditions. During the measurement period in 2017–2018, we retrieved LDSA data measured by Pegasor AQ Urban and other variables at a street canyon (SC, average LDSA = 19.7 ± 11.3 µm2 cm−3) site and an urban background (UB, average LDSA = 11.2 ± 7.1 µm2 cm−3) site in Helsinki, Finland. For the continuous estimation of LDSA, the IAME model was automatised to select the best combination of input variables, including a maximum of three fixed effect variables and three time indictors as random effect variables. Altogether, 696 submodels were generated and ranked by the coefficient of determination (R2), mean absolute error (MAE) and centred root-mean-square difference (cRMSD) in order. At the SC site, the LDSA concentrations were best estimated by mass concentration of particle of diameters smaller than 2.5 µm (PM2.5), total particle number concentration (PNC) and black carbon (BC), all of which are closely connected with the vehicular emissions. At the UB site, the LDSA concentrations were found to be correlated with PM2.5, BC and carbon monoxide (CO). The accuracy of the overall model was better at the SC site (R2=0.80, MAE = 3.7 µm2 cm−3) than at the UB site (R2=0.77, MAE = 2.3 µm2 cm−3), plausibly because the LDSA source was more tightly controlled by the close-by vehicular emission source. The results also demonstrated that the additional adjustment by taking random effects into account improved the sensitivity and the accuracy of the fixed effect model. Due to its adaptive input selection and inclusion of random effects, IAME could fill up missing data or even serve as a network of virtual sensors to complement the measurements at reference stations.Peer reviewe