Highest Average PM2 Research Articles

Temporal variation of the PM2.5/PM10 ratio and its association with meteorological factors in a South American megacity: Metropolitan Area of Lima-Callao, Peru.

The Metropolitan Area of Lima-Callao (MALC) is a South American megacity that has suffered a serious deterioration in air quality due to high levels of particulate matter (PM2.5 and PM10). Studies on the behavior of the PM2.5/PM10 ratio and its temporal variability in relation to meteorological parameters are still very limited. The objective of this study was to analyze the temporal trends of the PM2.5/PM10 ratio, its temporal variability, and its association with meteorological variables over a period of 5years (2015-2019). For this, the Theil-Sen estimator, bivariate polar plots, and correlation analysis were used. The regions of highest mean concentrations of PM2.5 and PM10 were identified at eastern Lima (ATE station-41.2µg/m3) and southern Lima (VMT station-126.7µg/m3), respectively. The lowest concentrations were recorded in downtown Lima (CDM station-16.8µg/m3 and 34.0µg/m3, respectively). The highest average PM2.5/PM10 ratio was found at the CDM station (0.55) and the lowest at the VMT station (0.27), indicating a predominance of emissions from the vehicular fleet within central Lima and a greater emission of coarse particles by resuspension in southern Lima. The temporal progression of the ratio of PM2.5/PM10 showed positive and highly significant trends in northern and central Lima with values of 0.03 and 0.1 units of PM2.5/PM10 per year, respectively. In the southern region of Lima, the trend was also significant, showcasing a value of 0.02 units of PM2.5/PM10 per year. At the hourly and monthly level, the PM2.5/PM10 ratio presented a negative and significant correlation with wind speed and air temperature, and a positive and significant correlation with relative humidity. These findings offer insights into identifying the sources of PM pollution and are useful for implementing regulations to reduce air emissions considering both anthropogenic sources and meteorological dispersion patterns.

Characterization of spatial-temporal distribution and microenvironment source contribution of PM2.5 concentrations using a low-cost sensor network with artificial neural network/kriging techniques

Low-cost sensors (LCS) network is widely used to improve the resolution of spatial-temporal distribution of air pollutant concentrations in urban areas. However, studies on air pollution sources contribution to the microenvironment, especially in industrial and mix-used housing areas, still need to be completed. This study investigated the spatial-temporal distribution and source contributions of PM2.5 in the urban area based on 6-month of the LCS network datasets. The Artificial Neural Network (ANN) was used to calibrate the measured PM2.5 by the LCS network. The calibrated PM2.5 were shown to agree with reference PM2.5 measured by the BAM-1020 with R2 of 0.85, MNE of 30.91%, and RMSE of 3.73 μg/m3, which meet the criteria for hotspot identification and personal exposure study purposes. The Kriging method was further used to establish the spatial-temporal distribution of PM2.5 concentrations in the urban area. Results showed that the highest average PM2.5 concentration occurred during autumn and winter due to monsoon and topographic effects. From a diurnal perspective, the highest level of PM2.5 concentration was observed during the daytime due to heavy traffic emissions and industrial production. Based on the present ANN-based microenvironment source contribution assessment model, temples, fried chicken shops, traffic emissions in shopping and residential zones, and industrial activities such as the mechanical manufacturing and precision metal machining were identified as the sources of PM2.5. The numerical algorithm coupled with the LCS network presented in this study is a practical framework for PM2.5 hotspots and source identification, aiding decision-makers in reducing atmospheric PM2.5 concentrations and formulating regional air pollution control strategies.

Analysis of spatiotemporal PM2.5 concentration patterns in Changwon, Korea, using low-cost PM2.5 sensors

Data of particulate matter (PM) of particle size 2.5 (PM2.5) obtained from September 2017 to December 2019 using 247 low-cost PM2.5 sensors were analyzed to determine the spatiotemporal characteristics of PM2.5 in Changwon City, South Korea. The highest average PM2.5 concentration (33 μg/m3) was observed in winter (November to March); the lowest average concentration (18.60 μg/m3) was measured in summer (June to October). The PM2.5 concentrations were the highest (31.81 μg/m3) during the morning rush hour (06:00 to 09:00 h) and lowest (23.82 μg/m3) in the afternoon (15:00 to 18:00 h). In agricultural areas, the highest average PM2.5 concentrations (37.26 and 34.01 μg/m3) were observed in February from 06:00 to 09:00 h. High PM2.5 concentrations were recorded at similar times in agricultural areas. This finding was because activities generating PM2.5, such as illegal incineration, occur in these areas. These high concentrations could cause damage to nearby vulnerable groups, such as elderly people. The results of this study could be useful for urban and environmental planning, reducing PM2.5 concentrations, and promoting sustainable development in urban areas.

In-kitchen aerosol exposure in twelve cities across the globe.

Poor ventilation and polluting cooking fuels in low-income homes cause high exposure, yet relevant global studies are limited. We assessed exposure to in-kitchen particulate matter (PM2.5 and PM10) employing similar instrumentation in 60 low-income homes across 12 cities: Dhaka (Bangladesh); Chennai (India); Nanjing (China); Medellín (Colombia); São Paulo (Brazil); Cairo (Egypt); Sulaymaniyah (Iraq); Addis Ababa (Ethiopia); Akure (Nigeria); Blantyre (Malawi); Dar-es-Salaam (Tanzania) and Nairobi (Kenya). Exposure profiles of kitchen occupants showed that fuel, kitchen volume, cooking type and ventilation were the most prominent factors affecting in-kitchen exposure. Different cuisines resulted in varying cooking durations and disproportional exposures. Occupants in Dhaka, Nanjing, Dar-es-Salaam and Nairobi spent>40% of their cooking time frying (the highest particle emitting cooking activity) compared with∼68% of time spent boiling/stewing in Cairo, Sulaymaniyah and Akure. The highest average PM2.5 (PM10) concentrations were in Dhaka 185±48 (220±58) μg m-3 owing to small kitchen volume, extensive frying and prolonged cooking compared with the lowest in Medellín 10±3 (14±2) μg m-3. Dual ventilation (mechanical and natural) in Chennai, Cairo and Sulaymaniyah reduced average in-kitchen PM2.5 and PM10 by 2.3- and 1.8-times compared with natural ventilation (open doors) in Addis Ababa, Dar-es-Salam and Nairobi. Using charcoal during cooking (Addis Ababa, Blantyre and Nairobi) increased PM2.5 levels by 1.3- and 3.1-times compared with using natural gas (Nanjing, Medellin and Cairo) and LPG (Chennai, Sao Paulo and Sulaymaniyah), respectively. Smaller-volume kitchens (<15m3; Dhaka and Nanjing) increased cooking exposure compared with their larger-volume counterparts (Medellin, Cairo and Sulaymaniyah). Potential exposure doses were highest for Asian, followed by African, Middle-eastern and South American homes. We recommend increased cooking exhaust extraction, cleaner fuels, awareness on improved cooking practices and minimising passive occupancy in kitchens to mitigate harmful cooking emissions.

Personal Exposure and Inhaled Dose Estimation of Air Pollutants during Travel between Albany, NY and Boston, MA

Out of eight deaths caused worldwide, one death is caused due to air pollution exposure, making it one of the top global killers. Personal exposure measurement for real-time monitoring has been used for inhaled dose estimation during various modes of workplace commuting. However, dose-exposure studies during long commutes are scarce and more information on inhaled doses is needed. This study focuses on personal exposures to size-fractionated particulate matter (PM1, PM2.5, PM4, PM7, PM10, TSP) and black carbon (BC) inside a bus traveling more than 270 kms on a highway between Albany, NY and Boston, MA. Measurements were also made indoors, outdoors, and while walking in each city. Mean PM (PM1, PM2.5, PM4, PM7, PM10, TSP) and mean BC concentrations were calculated to estimate the inhaled exposure dose. The highest average PM2.5 and PM10 exposures concentrations were 30 ± 12 and 111 ± 193 µg/m3, respectively, during Boston to Albany. Notably, personal exposure to BC on a bus from Albany to Boston (5483 ± 2099 ng/m3) was the highest measured during any commute. The average inhaled dose for PM2.5 during commutes ranged from 0.018 µg/km to 0.371 µg/km. Exposure concentrations in indoor settings (average PM2.5 = 37 ± 55 µg/m3, PM10 = 78 ± 82 µg/m3, BC = 5695 ± 1774 ng/m3) were higher than those in outdoor environments. Carpeted flooring, cooking, and vacuuming all tended to increase the indoor particulate level. A high BC concentration (1583 ± 1004 ng/m3) was measured during walking. Typical concentration profiles in long-haul journeys are presented.

Spatial distribution of fine and coarse particulate matter during a southwest monsoon in Peninsular Malaysia

This study aimed to determine the spatial distribution of PM2.5 and PM10 collected in four regions (North, Central, South and East Coast) of Peninsular Malaysia during the southwest monsoon. Concurrent measurements of PM2.5 and PM10 were performed using a high volume sampler (HVS) for 24 h (August to September 2018) collecting a total of 104 samples. All samples were then analysed for water soluble inorganic ions (WSII) using ion chromatography, trace metals using inductively coupled plasma-mass spectroscopy (ICP-MS) and polycyclic aromatic hydrocarbon (PAHs) using gas chromatography-mass spectroscopy (GC-MS). The results showed that the highest average PM2.5 concentration during the sampling campaign was in the North region (33.2 ± 5.3 μg m−3) while for PM10 the highest was in the Central region (38.6 ± 7.70 μg m−3). WSII recorded contributions of 22% for PM2.5 and 20% for PM10 mass, with SO42− the most abundant species with average concentrations of 1.83 ± 0.42 μg m−3 (PM2.5) and 2.19 ± 0.27 μg m−3 (PM10). Using a Positive Matrix Factorization (PMF) model, soil fertilizer (23%) was identified as the major source of PM2.5 while industrial activity (25%) was identified as the major source of PM10. Overall, the studied metals had hazard quotients (HQ) value of <1 indicating a very low risk of non-carcinogenic elements while the highest excess lifetime cancer risk (ELCR) was recorded for Cr VI in the South region with values of 8.4E-06 (PM2.5) and 6.6E-05 (PM10). The incremental lifetime cancer risk (ILCR) calculated from the PAH concentrations was within the acceptable range for all regions.

Indoor PM2.5 and its Polycyclic Aromatic Hydrocarbons in Relation with Incense Burning

This study aims to determine fine particulate matter (PM2.5) and polycyclic aromatic hydrocarbons (PAHs) emitted from incense burning to assess human health risk. PM2.5 samples were collected for 8 hrs and 24 hrs during special occasions and normal period from two shrines in the city of Chiang Mai, Thailand. PM2.5-bound PAHs were extracted and analyzed by GC-MS. The highest average PM2.5 concentrations were found during Chinese New Year (625±147 µg/m3 (8 hrs) and 406±159 µg/m3 (24 hrs)). The highest total PAHs concentrations were also found during the same period (168±60 ng/m3 for 8 hrs and 102±26 and for 24 hrs). Concentrations of PM2.5 and carcinogenic-PAHs were highly correlated (r = 0.451-0.802) and were high during special occasions particularly during Chinese New Year due to high number of visitors and amount of incense being burned. The toxicity equivalent (TEQ) values were also relatively high during Chinese New Year (31-32 ng/m3 (8 hrs) and 10-20 ng/m3 (24 hrs)). It can be revealed that incense burning emits air pollutants and can increase degree of indoor air pollution and human health risk.

PM2.5 particulates and particulate-bound mercury Hg(p) concentrations in a mixed urban, residential, traffic-heavy, and industrial site

ABSTRACTThis study analyzes the concentrations of ambient fine particulates and particulate-bound mercury Hg(p) at sampling sites in various areas in central Taiwan during a sampling period from January to April 2015. A Wilbur PM2.5 sampler was used to collect PM2.5 from ambient air and a DMA-80 direct mercury analyzer was used to analyze the concentration of mercury Hg(p) that was attached to PM2.5. The results show that the mean PM2.5 and Hg(p) concentrations were highest in March. The Chinese Lantern Festival and the “Faithful Pilgrims Who Follow Matsu through Taiwan” are hosted in this month. Many fireworks and firecrackers are let off at this time, releasing particulate matter into the atmosphere, increasing the concentrations of atmospheric particulate pollutants. The highest average PM2.5 concentrations were measured in January. A dust storm in Mainland China may have been responsible for this result. February saw the lowest mean PM2.5 recorded in this study, possibly because commercial districts and factories were closed in February for Chinese Lunar New Year. April saw the highest mean wind speed, diluting particulate pollutants in ambient air.

Impact of Asphalt Mixture Composition on Particulate Matter Production

Road traffic is one of the main sources of particulate matter. Nevertheless, the traffic volume is still increasing and has unpleasant impact on longevity of the pavements and the environment. Vehicle motions cause mechanical wearing of asphalt pavement surface by vehicle tires. The aim of paper is to confirm the abrasion of pavement surface as a source of particulate matter and to verify the impact of the composition of asphalt mixture on particulate matter production. The findings from chemical analyses of basic materials and intercepted particulate matter are applied in the research and used for verification of this impact. The research deals with abrasion of bituminous wearing courses of pavements. Each of the tested samples is specific in its composition - the type of bituminous binder, the amount of bituminous binder, type of aggregate, different lines of aggregate granularity. The particulate matter (PM) measurements were performed in laboratory conditions and the asphalt mixture samples were rutted in wheel tracking machine. The paper presents comparison of rutted asphalt samples in terms of PM mass concentrations and chemical composition. In the asphalt mixture sample with the highest average PM2.5 mass concentration (13.51μg/m3) the aggregates melaphyre and dolomite were used. On the other hand, the lowest average PM2.5 mass concentration (7.21μg/m3) was measured for the asphalt mixture with aggregate siliceous limestone.

Racial isolation and exposure to airborne particulate matter and ozone in understudied US populations: Environmental justice applications of downscaled numerical model output

BackgroundResearchers and policymakers are increasingly focused on combined exposures to social and environmental stressors, especially given how often these stressors tend to co-locate. Such exposures are equally relevant in urban and rural areas and may accrue disproportionately to particular communities or specific subpopulations. ObjectivesTo estimate relationships between racial isolation (RI), a measure of the extent to which minority racial/ethnic group members are exposed to only one another, and long-term particulate matter with an aerodynamic diameter of <2.5μ (PM2.5) and ozone (O3) levels in urban and nonurban areas of the eastern two-thirds of the US. MethodsLong-term (5year average) census tract-level PM2.5 and O3 concentrations were calculated using output from a downscaler model (2002–2006). The downscaler uses a linear regression with additive and multiplicative bias coefficients to relate ambient monitoring data with gridded output from the Community Multi-scale Air Quality (CMAQ) model. A local, spatial measure of RI was calculated at the tract level, and tracts were classified by urbanicity, RI, and geographic region. We examined differences in estimated pollutant exposures by RI, urbanicity, and demographic subgroup (e.g., race/ethnicity, education, socioeconomic status, age), and used linear models to estimate associations between RI and air pollution levels in urban, suburban, and rural tracts. ResultsHigh RI tracts (≥80th percentile) had higher average PM2.5 levels in each category of urbanicity compared to low RI tracts (<20th percentile), with the exception of the rural West. Patterns in O3 levels by urbanicity and RI differed by region. Linear models indicated that PM2.5 concentrations were significantly and positively associated with RI. The largest association between PM2.5 and RI was observed in the rural Midwest, where a one quintile increase in RI was associated with a 0.90μg/m3 (95% confidence interval: 0.83, 0.99μg/m3) increase in PM2.5 concentration. Associations between O3 and RI in the Northeast, Midwest and West were positive and highest in suburban and rural tracts, even after controlling for potential confounders such as percentage in poverty. ConclusionRI is associated with higher 5year estimated PM2.5 concentrations in urban, suburban, and rural census tracts, adding to evidence that segregation is broadly associated with disparate air pollution exposures. Disproportionate burdens to adverse exposures such as air pollution may be a pathway to racial/ethnic disparities in health.

Abstract 36: Mapping and Multilevel Modeling of Climate Change and Air Pollution With Risk of Stroke in the United States and China: Findings From the Drexel-SARI Low Carbon and Healthy City Study

Introduction and Hypothesis: Stroke is a leading cause of death and the major cause of disability in the world. However, few studies applied multilevel regression techniques to explore the association of stroke risk with climate change and air pollution. In the study, we aimed to test the hypothesis that the disproportionately distributed stroke rates across the counties and cities within a country are significantly associated with air pollution and temperature. Methods: We used data from U.S. 1118 counties in 49 states, which had estimated measures of particulate matter (PM)2.5 for the years 2010-2013, and data from China 120 cities in 32 provinces (including 4 municipalities), which had measures of Air Pollution Index (API) for the years 2012-2013. We assessed the association between air quality and prevalence of stroke using spatial mapping, autocorrelation and multilevel regression models. Results: Findings from the U.S. show that the highest average PM2.5 level was in July (10.2 μg/m3) and the lowest in October (7.63 μg/m3) for the years 2010-2013. Annual average PM2.5 levels were significantly different across the 1118 counties, and were significantly associated with stroke rates. Multilevel regression analysis indicated that the prevalence of stroke significantly increased by 1.19% for every 10 μg/m3 increase of PM2.5 (p&lt;0.001). Significant variability in PM2.5 by states was observed (p=0.019). More than 70% of the variation in stroke rates existed across the counties (p=0.017) and 18.7% existed across the states (p=0.047). In China, the highest API was observed in the month of December, with a result of 75.76 in 2012 and 97.51 in 2013. The lowest API was observed in July, with a result of 51.21 in 2012, and 54.23 in 2013. Prevalence of stroke was significantly higher in cities with higher API concentrations. The associations between air quality and risk of stroke were significantly mediated by temperatures. Conclusions: The study, using nationally representative data, is one of the first studies to address a positive and complex association between air quality and prevalence of stroke, and a potential interaction effect of temperatures on the air - stroke association.

Spatial variability in ambient atmospheric fine and coarse mode aerosols over Indo-Gangetic plains, India and adjoining oceans during the onset of summer monsoons, 2014

Enhanced transport of dust with the prevailing mid to upper tropospheric westerly winds from arid regions in South-west Asia and North-west India into the Indo-Gangetic Plain (IGP) and the influx of marine aerosol from the Arabian Sea (AS), Tropical Indian Ocean (TIO) and Southern Bay of Bengal (SBoB) into India along with the low level south-west wind flow during the onset of the South-west (SW) monsoon, 2014 was observed in campaign mode. Ambient airborne particulates (PM2.5 and PM10) were collected at 9 sites in and around IGP, India, viz. Patiala, Delhi, Lucknow, Varanasi, Giridih, Kolkata, Darjeeling, Bhubaneswar and Nagpur; over AS, TIO and SBoB providing a glimpse into the aerosol loading and its transport mechanisms. The highest average PM2.5 (61.8 ± 18.6 μg m−3) and PM10 (182.2 ± 58.0 μg m−3) mass concentrations were recorded at Delhi (upper IGP) and Lucknow (middle IGP) respectively. Average PM2.5 (18.1 ± 10.1 μg m−3) and PM10 (39.6 ± 15.8 μg m−3) levels recorded over the open oceanic regions in AS, TIO and SBoB were much lower than those observed over the land stations and the average PM2.5 recorded over coastal AS and SBoB (49.1 ± 28.7 μg m−3).Cluster analysis, Potential Source Contribution Function (PSCF) and Concentration Weighted Trajectory (CWT) analysis portray that PM2.5 and PM10 levels at the land stations were influenced by weak to moderate contributions from AS, BoB, the arid South-west Asia and North-west India, peninsular India and from the polluted IGP region.

A case study of air quality above an urban roof top vegetable farm

The effect of elevation and rooftop configuration on local air quality was investigated at the Brooklyn Grange rooftop farm during a short-term observational campaign. Using multiple particle counters and sonic anemometers deployed along vertical gradients, we found that PM2.5 concentration decayed with height above the street. Samples adjacent to the street had the highest average PM2.5 concentration and frequent stochastic spikes above background. Rooftop observations 26 m above ground showed 7–33% reductions in average PM2.5 concentration compared with the curbside and had far fewer spikes. A relationship between the vertical extinction rate of PM2.5 and atmospheric stability was found whereby less unstable atmosphere and greater wind shear led to greater PM2.5 extinction due to damped vertical motion of air.

Atmospheric Fine and Coarse Mode Aerosols at Different Environments of India and the Bay of Bengal During Winter-2014: Implications of a Coordinated Campaign

In this paper, we present mass concentrations of particulate matter [PM2.5, PM10 size fractions and total suspended particulates (TSP)] measured simultaneously over land stations (Kullu, Patiala, Delhi, Ajmer, Agra, Lucknow, Varanasi, Giridih, Kolkata, Darjeeling, Jorhat, Itanagar, Imphal, Bhubaneswar, and Kadapa), mostly distributed across the Indo-Gangetic plain (IGP) of India as well as in the marine atmosphere over Bay of Bengal (BoB) in the period from 20 January to 3 February, 2014. The main objective of this study was to quantify the continental outflow of particulates (PM2.5, PM10 and TSP) from IGP and associated regions into the BoB along with low level north-east wind flow during winter monsoon period. The present study provides a glimpse of the aerosol loading over the IGP region. During this campaign, the highest average PM2.5 (187.8 ± 36.5 µg m−3, range 125.6–256.2 µg m−3), PM10 (272.6 ± 102.9 µg m−3, range 147.6–520.1 µg m−3) and TSP (325.0 ± 71.5 µg m−3, range 220.4–536.6 µg m−3) mass concentrations were recorded at Varanasi, Kolkata and Lucknow over middle and lower IGP regions. The PM2.5 (average 41.3 ± 11.9 µg m−3; range 15.0–54.4 µg m−3), PM10 (average 53.9 ± 18.9 µg m−3; range 30.1–82.1 µg m−3) and TSP (average 78.8 ± 29.7 µg m−3; range 49.1–184.5 µg m−3) loading over BoB were found to be comparable to land stations and suggests possible continental outflow. Over the continental region, the highest PM2.5/PM10 ratio was recorded at Delhi (0.87). The PM2.5/PM10 ratio over BoB (0.77) was found to be quite high and comparable to Varanasi (0.80) and Agra (0.79).

The Impact of Long-Term Exposure to Air Pollution on Conduit Artery and Microvascular Function

Background: Long-term exposure to ambient air pollution, including fine particulate matter (PM2.5), has been associated with cardiovascular morbidity and mortality. Impaired vascular responses may in part explain these findings, but few studies have examined chronic effects of long-term exposures incorporating measures of both conduit artery and microvascular function in a large population-based study. Aims: We evaluated the impact of residential proximity to primary or secondary highway (&lt;100m, 100-200m, 200-400m, &gt;400m) and long term exposure to PM2.5 (1-year average) on brachial artery flow mediated dilation (FMD) and reactive hyperemia in the Framingham Offspring and Third Generation Cohorts. Methods: FMD (%) and hyperemic flow velocity (cm/s) were assessed in the Offspring 7th (1998-2001) and Third Generation 1st (2001-2005) exams (n=5,133). We evaluated associations between residential proximity to major roads and spatially resolved estimated PM2.5 from land use and satellite images with FMD and hyperemic flow using linear regression. Results: Compared to living further than 400m from a major road, living within 100m was associated with -0.31% (95% CI: -0.53, -0.09) lower FMD and -1.49 cm/s (95% CI: -2.9,- 0.09) lower hyperemic flow velocity in models adjusted for age, sex, body mass index, smoking status, long-term time trends, season, cohort, and indicators of socioeconomic position. A 1 µg/m3 higher average PM2.5 was associated with -0.09% (95% CI: -0.24, 0.06) lower FMD, and -1.07 cm/s (95% CI: -2.0, -0.12) lower hyperemic flow. Results were not materially altered after further adjustment for potential mediators. The magnitude of estimates for hyperemic flow became stronger when we excluded smokers and participants using antihypertensive or lipid-lowering medications. Conclusions: In a large, community-based cohort, measures of long-term exposure to air pollution were associated with impaired conduit and microvascular function.

Personal exposure of commuters in public transport to PM2.5 and fine particle counts

To investigate fine particulate air pollution generated by public transport and its microenvironment, PM2.5 measurements and particle number counts for six particle size ranges (0.3–0.5 µm, >0.5–1.0 µm, >1.0–3.0 µm, >3.0– 5.0 µm, >5.0–10 µm and >10 µm) were obtained for four public transport modes: bus, metro–bus, car and walking. The measurements were repeated for each transport mode twice a day for 7–10 measurement days. The highest average PM2.5 concentration was measured inside a bus (106 µg/m3) during rush hours. The highest single peak measurement was a concentration of 316 µg/m3 for walking during non–rush hours. The PM2.5 level in a car with the air conditioning fan off was approximately 2.5 times lower than the level with the air conditioning fan on. Moderate correlations were found between PM2.5 concentrations and wind speed. Weak correlations were found between PM2.5 concentrations, relative humidity and temperature. The results showed that the diameters of most of the particles were smaller than 0.5 µm, regardless of the transport mode. The average fine particle number (size range 0.3–0.5) for all transport modes ranged from 54 647 to 209 746 particles/103 cm3 during rush hours and from 49 423 to 184 866 particles/103 cm3 during non–rush hours.

Spatial variation of PM2.5, PM10, PM2.5 absorbance and PMcoarse concentrations between and within 20 European study areas and the relationship with NO2 – Results of the ESCAPE project

The ESCAPE study (European Study of Cohorts for Air Pollution Effects) investigates relationships between long-term exposure to outdoor air pollution and health using cohort studies across Europe. This paper analyses the spatial variation of PM2.5, PM2.5 absorbance, PM10 and PMcoarse concentrations between and within 20 study areas across Europe.We measured NO2, NOx, PM2.5, PM2.5 absorbance and PM10 between October 2008 and April 2011 using standardized methods. PMcoarse was determined as the difference between PM10 and PM2.5. In each of the twenty study areas, we selected twenty PM monitoring sites to represent the variability in important air quality predictors, including population density, traffic intensity and altitude. Each site was monitored over three 14-day periods spread over a year, using Harvard impactors. Results for each site were averaged after correcting for temporal variation using data obtained from a reference site, which was operated year-round.Substantial concentration differences were observed between and within study areas. Concentrations for all components were higher in Southern Europe than in Western and Northern Europe, but the pattern differed per component with the highest average PM2.5 concentrations found in Turin and the highest PMcoarse in Heraklion. Street/urban background concentration ratios for PMcoarse (mean ratio 1.42) were as large as for PM2.5 absorbance (mean ratio 1.38) and higher than those for PM2.5 (1.14) and PM10 (1.23), documenting the importance of non-tailpipe emissions. Correlations between components varied between areas, but were generally high between NO2 and PM2.5 absorbance (average R2 = 0.80). Correlations between PM2.5 and PMcoarse were lower (average R2 = 0.39). Despite high correlations, concentration ratios between components varied, e.g. the NO2/PM2.5 ratio varied between 0.67 and 3.06.In conclusion, substantial variability was found in spatial patterns of PM2.5, PM2.5 absorbance, PM10 and PMcoarse. The highly standardized measurement of particle concentrations across Europe will contribute to a consistent assessment of health effects across Europe.

Exposure to Particulate Matter, Kathmandu Valley, Nepal

PP-29-017 Abstract: Background/Aims: Air pollution, especially from traffic, is a serious problem in Kathmandu Valley. The project compared personal particulate matter (PM2.5) exposure and respiratory health based on occupation and roadway proximity. Additionally, temporal and spatial trends for ambient PM10 were examined. Methods: We investigated personal PM2.5 exposure and respiratory health for 3 groups: (1) traffic personnel (TP), (2) indoor officer workers near main roads (IOW_NMR), (3) indoor office workers away from main roads (IOW_AMR). At 3 locations (urban, urban residential, and semiurban), we recruited 6 TP, 3 IOW_NMR, and 3 IOW_AMR. For each participant, personal PM2.5 monitoring, time activity dairy, respiratory health questionnaire, and spirometer test was performed from 28 June 2009 to 7 August 2009. Traffic count was measured during peak traffic hours. Ambient PM10 data (2002–2007) from 6 government monitoring sites was assessed for temporal and spatial variation. Results: TP had the highest average PM2.5 exposure (50.16 μg/ma) with 78.3% of study days exceeding World Health Organization guidelines (25 μg/ma). The IOW_NMR average PM2.5 levels were 46.18 μg/ma, with 53.9% of days exceeding guidelines. The IOW_AMR had the lowest levels at 25.23 μg/ma, exceeding guidelines on 20.8% of days. Exposure was higher during morning rush hours than evening rush hours for all participant types and areas (71.9% of days for TP; 83.9% for IOW at urban settings). Similar prevalence of respiratory symptoms and lung function was observed across participant types. A weak association was noted between PM2.5 and lung function. Government PM10 monitors from 2003 to 2007 exceeded Nepal's daily PM10 standard (120 μg/ma) 48.8%–80.5% of days annually for inner and 3.36%–29.3% for outer city sites. Conclusion: High PM2.5 exposure was observed, with levels differing by location, time of day, and occupation. Findings indicate that air pollution is likely to have substantial health impacts in this region, especially for traffic police. A larger dataset is required to evaluate links between air pollution and health.