Examining the status of improved air quality in world cities due to COVID-19 led temporary reduction in anthropogenic emissions.

Clean air is a fundamental necessity for human health and well-being. Anthropogenic emissions that are harmful to human health have been reduced substantially under COVID-19 lockdown. Satellite remote sensing for air pollution assessments can be highly effective in public health research because of the possibility of estimating air pollution levels over large scales. In this study, we utilized both satellite and surface measurements to estimate air pollution levels in 20 cities across the world. Google Earth Engine (GEE) and Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) application were used for both spatial and time-series assessment of tropospheric Nitrogen Dioxide (NO2) and Carbon Monoxide (CO) statuses during the study period (1 February to May 11, 2019 and the corresponding period in 2020). We also measured Population-Weighted Average Concentration (PWAC) of particulate matter (PM2.5 and PM10) and NO2 using gridded population data and in-situ air pollution estimates. We estimated the economic benefit of reduced anthropogenic emissions using two valuation approaches: (1) the median externality value coefficient approach, applied for satellite data, and (2) the public health burden approach, applied for in-situ data. Satellite data have shown that ~28 tons (sum of 20 cities) of NO2 and ~184 tons (sum of 20 cities) of CO have been reduced during the study period. PM2.5, PM10, and NO2 are reduced by ~37 (µg/m3), 62 (µg/m3), and 145 (µg/m3), respectively. A total of ~1310, ~401, and ~430 premature cause-specific deaths were estimated to be avoided with the reduction of NO2, PM2.5, and PM10. The total economic benefits (Billion US$) (sum of 20 cities) of the avoided mortality are measured as ~10, ~3.1, and ~3.3 for NO2, PM2.5, and PM10, respectively. In many cases, ground monitored data was found inadequate for detailed spatial assessment. This problem can be better addressed by incorporating satellite data into the evaluation if proper quality assurance is achieved, and the data processing burden can be alleviated or even removed. Both satellite and ground-based estimates suggest the positive effect of the limited human interference on the natural environments. Further research in this direction is needed to explore this synergistic association more explicitly.

Antibiotic choice in UK general practice: rates and drivers of potentially inappropriate antibiotic prescribing.

OBJECTIVES: To identify the rates of potentially inappropriate antibiotic choice when prescribing for common infections in UK general practices. To examine the predictors of such prescribing and the clustering effects at the practice level. METHODS: The rates of potentially inappropriate antibiotic choice were estimated using 1 151 105 consultations for sinusitis, otitis media and externa, upper respiratory tract infection (URTI) and lower respiratory tract infection (LRTI) and urinary tract infection (UTI), using the Clinical Practice Research Datalink (CPRD). Multilevel logistic regression was used to identify the predictors of inappropriate prescribing and to quantify the clustering effect at practice level. RESULTS: The rates of potentially inappropriate prescriptions were highest for otitis externa (67.3%) and URTI (38.7%) and relatively low for otitis media (3.4%), sinusitis (2.2%), LRTI (1.5%) and UTI in adults (2.3%) and children (0.7%). Amoxicillin was the most commonly prescribed antibiotic for all respiratory tract infections, except URTI. Amoxicillin accounted for 62.3% of prescriptions for otitis externa and 34.5% of prescriptions for URTI, despite not being recommended for these conditions. A small proportion of the variation in the probability of an inappropriate choice was attributed to the clustering effect at practice level (8% for otitis externa and 23% for sinusitis). Patients with comorbidities were more likely to receive a potentially inappropriate antibiotic for URTI, LRTI and UTI in adults. Patients who received any antibiotic in the 12 months before consultation were more likely to receive a potentially inappropriate antibiotic for all conditions except otitis externa. CONCLUSIONS: Antibiotic prescribing did not always align with prescribing guidelines, especially for URTIs and otitis externa. Future interventions might target optimizing amoxicillin use in primary care.

Antibiotic prescribing for common infections in UK general practice: variability and drivers.

OBJECTIVES: To examine variations across general practices and factors associated with antibiotic prescribing for common infections in UK primary care to identify potential targets for improvement and optimization of prescribing. METHODS: Oral antibiotic prescribing for common infections was analysed using anonymized UK primary care electronic health records between 2000 and 2015 using the Clinical Practice Research Datalink (CPRD). The rate of prescribing for each condition was observed over time and mean change points were compared with national guideline updates. Any correlation between the rate of prescribing for each infectious condition was estimated within a practice. Predictors of prescribing were estimated using logistic regression in a matched patient cohort (1:1 by age, sex and calendar time). RESULTS: Over 8 million patient records were examined in 587 UK general practices. Practices varied considerably in their propensity to prescribe antibiotics and this variance increased over time. Change points in prescribing did not reflect updates to national guidelines. Prescribing levels within practices were not consistent for different infectious conditions. A history of antibiotic use significantly increased the risk of receiving a subsequent antibiotic (by 22%-48% for patients with three or more antibiotic prescriptions in the past 12 months), as did higher BMI, history of smoking and flu vaccinations. Other drivers for receiving an antibiotic varied considerably for each condition. CONCLUSIONS: Large variability in antibiotic prescribing between practices and within practices was observed. Prescribing guidelines alone do not positively influence a change in prescribing, suggesting more targeted interventions are required to optimize antibiotic prescribing in the UK.

The Fort Collins commuter study: Variability in personal exposure to air pollutants by microenvironment.

This study investigated the role of microenvironment on personal exposures to black carbon (BC), fine particulate mass (PM2.5 ), carbon monoxide (CO), and particle number concentration (PNC) among adult residents of Fort Collins, Colorado, USA. Forty-four participants carried a backpack containing personal monitoring instruments for eight nonconsecutive 24-hour periods. Exposures were apportioned into five microenvironments: Home, Work, Transit, Eateries, and Other. Personal exposures exhibited wide heterogeneity that was dominated by within-person variability (both day-to-day and between microenvironment variability). Linear mixed-effects models were used to compare mean personal exposures in each microenvironment, while accounting for possible within-person correlation. Mean personal exposures during Transit and at Eateries tended to be higher than exposures at Home, where participants spent the majority of their time. Compared to Home, mean exposures to BC in Transit were, on average, 129% [95% confidence interval: 101% 162%] higher and exposures to PNC were 180% [101% 289%] higher in Eateries.

Personal exposure to static and time-varying magnetic fields during MRI procedures in clinical practice in the UK.

BACKGROUND: MRI has developed into one of the most important medical diagnostic imaging modalities, but it exposes staff to static magnetic fields (SMF) when present in the vicinity of the MR system, and to radiofrequency and switched gradient electromagnetic fields if they are present during image acquisition. We measured exposure to SMF and motion-induced time-varying magnetic fields (TVMF) in MRI staff in clinical practice in the UK to enable extensive assessment of personal exposure levels and variability, which enables comparison to other countries. METHODS: 8 MRI facilities across National Health Service sites in England, Wales and Scotland were included, and staff randomly selected during the days when measurements were performed were invited to wear a personal MRI-compatible dosimeter and keep a diary to record all procedures and tasks performed during the measured shift. RESULTS: 98 participants, primarily radiographers (71%) but also other healthcare staff, anaesthetists and other medical staff were included, resulting in 149 measurements. Average geometric mean peak SMF and TVMF exposures were 448 mT (range 20-2891) and 1083 mT/s (9-12 355 mT/s), and were highest for radiographers (GM=559 mT and GM=734 mT/s). Time-weighted exposures to SMF and TVMF (GM=16 mT (range 5-64) and GM=14 mT/s (range 9-105)) and exposed-time-weighted exposures to SMF and TVMF (GM=27 mT (range 11-89) and GM=17 mT/s (range 9-124)) were overall relative low-primarily because staff were not in the MRI suite for most of their shifts-and did not differ significantly between occupations. CONCLUSIONS: These results are comparable to the few data available from the UK but they differ from recent data collected in the Netherlands, indicating that UK staff are exposed for shorter periods but to higher levels. These data indicate that exposure to SMF and TVMF from MRI scanners cannot be extrapolated across countries.

A multicentre study of air pollution exposure and childhood asthma prevalence: the ESCAPE project.

The aim of this study was to determine the effect of six traffic-related air pollution metrics (nitrogen dioxide, nitrogen oxides, particulate matter with an aerodynamic diameter <10 µm (PM10), PM2.5, coarse particulate matter and PM2.5 absorbance) on childhood asthma and wheeze prevalence in five European birth cohorts: MAAS (England, UK), BAMSE (Sweden), PIAMA (the Netherlands), GINI and LISA (both Germany, divided into north and south areas). Land-use regression models were developed for each study area and used to estimate outdoor air pollution exposure at the home address of each child. Information on asthma and current wheeze prevalence at the ages of 4-5 and 8-10 years was collected using validated questionnaires. Multiple logistic regression was used to analyse the association between pollutant exposure and asthma within each cohort. Random-effects meta-analyses were used to combine effect estimates from individual cohorts. The meta-analyses showed no significant association between asthma prevalence and air pollution exposure (e.g. adjusted OR (95%CI) for asthma at age 8-10 years and exposure at the birth address (n=10377): 1.10 (0.81-1.49) per 10 µg · m(-3) nitrogen dioxide; 0.88 (0.63-1.24) per 10 µg · m(-3) PM10; 1.23 (0.78-1.95) per 5 µg · m(-3) PM2.5). This result was consistently found in initial crude models, adjusted models and further sensitivity analyses. This study found no significant association between air pollution exposure and childhood asthma prevalence in five European birth cohorts.

Transient health symptoms of MRI staff working with 1.5 and 3.0 Tesla scanners in the UK.

OBJECTIVES: Recent studies have consistently shown that amongst staff working with MRI, transient symptoms directly attributable to the MRI system including dizziness, nausea, tinnitus, and concentration problems are reported. This study assessed symptom prevalence and incidence in radiographers and other staff working with MRI in healthcare in the UK. METHODS: One hundred and four volunteer staff from eight sites completed a questionnaire and kept a diary to obtain information on subjective symptoms and work practices, and wore a magnetic field dosimeter during one to three randomly selected working days. Incidence of MRI-related symptoms was obtained for all shifts and prevalence of MRI-related and reference symptoms was associated to explanatory factors using ordinal regression. RESULTS: Incident symptoms related to working with MRI were reported in 4% of shifts. Prevalence of MRI-related, but not reference symptoms were associated with number of hours per week working with MRI, shift length, and stress, but not with magnetic field strength (1.5 and 3 T) or measured magnetic field exposure. CONCLUSIONS: Reporting of prevalent symptoms was associated with longer duration of working in MRI departments, but not with measured field strength of exposure. Other factors related to organisation and stress seem to contribute to increased reporting of MRI-related symptoms. KEY POINTS: • Routine work with MRI is associated with increased reporting of transient symptoms • No link to the strength of the magnetic field was demonstrated. • Organisational factors and stress additionally contribute to reporting of MRI-related symptoms.

Meta-analysis of air pollution exposure association with allergic sensitization in European birth cohorts.

BACKGROUND: Evidence on the long-term effects of air pollution exposure on childhood allergy is limited. OBJECTIVE: We investigated the association between air pollution exposure and allergic sensitization to common allergens in children followed prospectively during the first 10 years of life. METHODS: Five European birth cohorts participating in the European Study of Cohorts for Air Pollution Effects project were included: BAMSE (Sweden), LISAplus and GINIplus (Germany), MAAS (Great Britain), and PIAMA (The Netherlands). Land-use regression models were applied to assess the individual residential outdoor levels of particulate matter with an aerodynamic diameter of less than 2.5 µm (PM2.5), the mass concentration of particles between 2.5 and 10 µm in size, and levels of particulate matter with an aerodynamic diameter of less than 10 µm (PM10), as well as measurement of the blackness of PM2.5 filters and nitrogen dioxide and nitrogen oxide levels. Blood samples drawn at 4 to 6 years of age, 8 to 10 years of age, or both from more than 6500 children were analyzed for allergen-specific serum IgE against common allergens. Associations were assessed by using multiple logistic regression and subsequent meta-analysis. RESULTS: The prevalence of sensitization to any common allergen within the 5 cohorts ranged between 24.1% and 40.4% at the age of 4 to 6 years and between 34.8% and 47.9% at the age of 8 to 10 years. Overall, air pollution exposure was not associated with sensitization to any common allergen, with odds ratios ranging from 0.94 (95% CI, 0.63-1.40) for a 1 × 10(-5) ∙ m(-1) increase in measurement of the blackness of PM2.5 filters to 1.26 (95% CI, 0.90-1.77) for a 5 µg/m(3) increase in PM2.5 exposure at birth address. Further analyses did not provide consistent evidence for a modification of the air pollution effects by sex, family history of atopy, or moving status. CONCLUSION: No clear associations between air pollution exposure and development of allergic sensitization in children up to 10 years of age were revealed.

Elemental composition of particulate matter and the association with lung function.

BACKGROUND: Negative effects of long-term exposure to particulate matter (PM) on lung function have been shown repeatedly. Spatial differences in the composition and toxicity of PM may explain differences in observed effect sizes between studies. METHODS: We conducted a multicenter study in 5 European birth cohorts-BAMSE (Sweden), GINIplus and LISAplus (Germany), MAAS (United Kingdom), and PIAMA (The Netherlands)-for which lung function measurements were available for study subjects at the age of 6 or 8 years. Individual annual average residential exposure to copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc within PM smaller than 2.5 µm (PM2.5) and smaller than 10 µm (PM10) was estimated using land-use regression models. Associations between air pollution and lung function were analyzed by linear regression within cohorts, adjusting for potential confounders, and then combined by random effects meta-analysis. RESULTS: We observed small reductions in forced expiratory volume in the first second, forced vital capacity, and peak expiratory flow related to exposure to most elemental pollutants, with the most substantial negative associations found for nickel and sulfur. PM10 nickel and PM10 sulfur were associated with decreases in forced expiratory volume in the first second of 1.6% (95% confidence interval = 0.4% to 2.7%) and 2.3% (-0.1% to 4.6%) per increase in exposure of 2 and 200 ng/m, respectively. Associations remained after adjusting for PM mass. However, associations with these elements were not evident in all cohorts, and heterogeneity of associations with exposure to various components was larger than for exposure to PM mass. CONCLUSIONS: Although we detected small adverse effects on lung function associated with annual average levels of some of the evaluated elements (particularly nickel and sulfur), lower lung function was more consistently associated with increased PM mass.

Evaluation of land use regression models for NO2 and particulate matter in 20 European study areas: the ESCAPE project.

Land use regression models (LUR) frequently use leave-one-out-cross-validation (LOOCV) to assess model fit, but recent studies suggested that this may overestimate predictive ability in independent data sets. Our aim was to evaluate LUR models for nitrogen dioxide (NO2) and particulate matter (PM) components exploiting the high correlation between concentrations of PM metrics and NO2. LUR models have been developed for NO2, PM2.5 absorbance, and copper (Cu) in PM10 based on 20 sites in each of the 20 study areas of the ESCAPE project. Models were evaluated with LOOCV and "hold-out evaluation (HEV)" using the correlation of predicted NO2 or PM concentrations with measured NO2 concentrations at the 20 additional NO2 sites in each area. For NO2, PM2.5 absorbance and PM10 Cu, the median LOOCV R(2)s were 0.83, 0.81, and 0.76 whereas the median HEV R(2) were 0.52, 0.44, and 0.40. There was a positive association between the LOOCV R(2) and HEV R(2) for PM2.5 absorbance and PM10 Cu. Our results confirm that the predictive ability of LUR models based on relatively small training sets is overestimated by the LOOCV R(2)s. Nevertheless, in most areas LUR models still explained a substantial fraction of the variation of concentrations measured at independent sites.

Development of land use regression models for particle composition in twenty study areas in Europe.

Land Use Regression (LUR) models have been used to describe and model spatial variability of annual mean concentrations of traffic related pollutants such as nitrogen dioxide (NO2), nitrogen oxides (NOx) and particulate matter (PM). No models have yet been published of elemental composition. As part of the ESCAPE project, we measured the elemental composition in both the PM10 and PM2.5 fraction sizes at 20 sites in each of 20 study areas across Europe. LUR models for eight a priori selected elements (copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)) were developed. Good models were developed for Cu, Fe, and Zn in both fractions (PM10 and PM2.5) explaining on average between 67 and 79% of the concentration variance (R(2)) with a large variability between areas. Traffic variables were the dominant predictors, reflecting nontailpipe emissions. Models for V and S in the PM10 and PM2.5 fractions and Si, Ni, and K in the PM10 fraction performed moderately with R(2) ranging from 50 to 61%. Si, NI, and K models for PM2.5 performed poorest with R(2) under 50%. The LUR models are used to estimate exposures to elemental composition in the health studies involved in ESCAPE.

Development of Land Use Regression models for PM(2.5), PM(2.5) absorbance, PM(10) and PM(coarse) in 20 European study areas; results of the ESCAPE project.

Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM(2.5), PM(2.5) absorbance, PM(10), and PM(coarse) were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R(2)) was 71% for PM(2.5) (range across study areas 35-94%). Model R(2) was higher for PM(2.5) absorbance (median 89%, range 56-97%) and lower for PM(coarse) (median 68%, range 32- 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R(2) was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R(2) results were on average 8-11% lower than model R(2). Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE.

Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach.

The socio-demographic factors have a substantial impact on the overall casualties caused by the Coronavirus (COVID-19). In this study, the global and local spatial association between the key socio-demographic variables and COVID-19 cases and deaths in the European regions were analyzed using the spatial regression models. A total of 31 European countries were selected for modelling and subsequent analysis. From the initial 28 socio-demographic variables, a total of 2 (for COVID-19 cases) and 3 (for COVID-19 deaths) key variables were filtered out for the regression modelling. The spatially explicit regression modelling and mapping were done using four spatial regression models such as Geographically Weighted Regression (GWR), Spatial Error Model (SEM), Spatial Lag Model (SLM), and Ordinary Least Square (OLS). Additionally, Partial Least Square (PLS) and Principal Component Regression (PCR) was performed to estimate the overall explanatory power of the regression models. For the COVID cases, the local R2 values, which suggesting the influences of the selected socio-demographic variables on COVID cases and death, were found highest in Germany, Austria, Slovenia, Switzerland, Italy. The moderate local R2 was observed for Luxembourg, Poland, Denmark, Croatia, Belgium, Slovakia. The lowest local R2 value for COVID-19 cases was accounted for Ireland, Portugal, United Kingdom, Spain, Cyprus, Romania. Among the 2 variables, the highest local R2 was calculated for income (R2 = 0.71), followed by poverty (R2 = 0.45). For the COVID deaths, the highest association was found in Italy, Croatia, Slovenia, Austria. The moderate association was documented for Hungary, Greece, Switzerland, Slovakia, and the lower association was found in the United Kingdom, Ireland, Netherlands, Cyprus. This suggests that the selected demographic and socio-economic components, including total population, poverty, income, are the key factors in regulating overall casualties of COVID-19 in the European region. In this study, the influence of the other controlling factors, such as environmental conditions, socio-ecological status, climatic extremity, etc. have not been considered. This could be the scope for future research.

Feasibility of Indonesia Family Life Survey Wave 5 (IFLS5) Data for Air Pollution Exposure-Response Study in Indonesia.

BACKGROUND: Air pollution is an important risk factor for the disease burden; however there is limited evidence in Indonesia on the effect of air pollution on health, due to lack of exposure and health outcome data. The objective of this study is to evaluate the potential use of the IFLS data for response part of urban-scale air pollution exposure-health response studies. METHODS: Relevant variables were extracted based on IFLS5 documentation review. Analysis of the spatial distribution of respondent, data completeness, prevalence of relevant health outcomes, and consistency or agreement evaluation between similar variables were performed. Power for ideal sample size was estimated. RESULTS: There were 58,304 respondents across 23 provinces, with the highest density in Jakarta (750/district). Among chronic conditions, hypertension had the highest prevalence (15-25%) with data completeness of 79-83%. Consistency among self-reported health outcome variables was 90-99%, while that with objective measurements was 42-70%. The estimated statistical power for studying air pollution effect on hypertension (prevalence = 17%) in Jakarta was approximately 0.6 (α = 0.1). CONCLUSIONS: IFLS5 data has potential use for epidemiological study of air pollution and health outcomes such as hypertension, to be coupled with high quality urban-scale air pollution exposure estimates, particularly in Jakarta.

Relationship between prescribing of antibiotics and other medicines in primary care: a cross-sectional study.

BACKGROUND: High levels of antibiotic prescribing are a major concern as they drive antimicrobial resistance. It is currently unknown whether practices that prescribe higher levels of antibiotics also prescribe more medicines in general. AIM: To evaluate the relationship between antibiotic and general prescribing levels in primary care. DESIGN AND SETTING: Cross-sectional study in 2014-2015 of 6517 general practices in England using NHS digital practice prescribing data (NHS-DPPD) for the main study, and of 587 general practices in the UK using the Clinical Practice Research Datalink for a replication study. METHOD: Linear regression to assess determinants of antibiotic prescribing. RESULTS: NHS-DPPD practices prescribed an average of 576.1 antibiotics per 1000 patients per year (329.9 at the 5th percentile and 808.7 at the 95th percentile). The levels of prescribing of antibiotics and other medicines were strongly correlated. Practices with high levels of prescribing of other medicines (a rate of 27 159.8 at the 95th percentile) prescribed 80% more antibiotics than low-prescribing practices (rate of 8815.9 at the 5th percentile). After adjustment, NHS-DPPD practices with high prescribing of other medicines gave 60% more antibiotic prescriptions than low-prescribing practices (corresponding to higher prescribing of 276.3 antibiotics per 1000 patients per year). Prescribing of non-opioid painkillers and benzodiazepines were also strong indicators of the level of antibiotic prescribing. General prescribing levels were a much stronger driver for antibiotic prescribing than other risk factors, such as deprivation. CONCLUSION: The propensity of GPs to prescribe medications generally is an important driver for antibiotic prescribing. Interventions that aim to optimise antibiotic prescribing will need to target general prescribing behaviours, in addition to specifically targeting antibiotics.

Antibiotic prescribing patterns in general medical practices in England: Does area matter?

Antimicrobial resistance is an important public health concern. As most antibiotics are prescribed in primary care, understanding prescribing patterns in General Medical (GP) practices is vital. The aim of this study was a spatial pattern analysis of antibiotic prescribing rates in GP practices in England and to examine the association of potential clusters with area level socio-economic deprivation. The pattern analysis identified a number of hot and cold spots of antibiotic prescribing, with hot spots predominantly in the North of England. Spatial regression showed that patient catchments of hot spot practices were significantly more deprived than patient catchments of cold spot practices, especially in the domains of income, employment, education and health. This study suggests the presence of area level drivers resulting in clusters of high and low prescribing. Consequently, area level strategies may be needed for antimicrobial stewardship rather than national level strategies.

An accurate filter loading correction is essential for assessing personal exposure to black carbon using an Aethalometer.

The AE51 micro-Aethalometer (microAeth) is a popular and useful tool for assessing personal exposure to particulate black carbon (BC). However, few users of the AE51 are aware that its measurements are biased low (by up to 70%) due to the accumulation of BC on the filter substrate over time; previous studies of personal black carbon exposure are likely to have suffered from this bias. Although methods to correct for bias in micro-Aethalometer measurements of particulate black carbon have been proposed, these methods have not been verified in the context of personal exposure assessment. Here, five Aethalometer loading correction equations based on published methods were evaluated. Laboratory-generated aerosols of varying black carbon content (ammonium sulfate, Aquadag and NIST diesel particulate matter) were used to assess the performance of these methods. Filters from a personal exposure assessment study were also analyzed to determine how the correction methods performed for real-world samples. Standard correction equations produced correction factors with root mean square errors of 0.10 to 0.13 and mean bias within ±0.10. An optimized correction equation is also presented, along with sampling recommendations for minimizing bias when assessing personal exposure to BC using the AE51 micro-Aethalometer.

The Fort Collins Commuter Study: Impact of route type and transport mode on personal exposure to multiple air pollutants.

Traffic-related air pollution is associated with increased mortality and morbidity, yet few studies have examined strategies to reduce individual exposure while commuting. The present study aimed to quantify how choice of mode and route type affects personal exposure to air pollutants during commuting. We analyzed within-person difference in exposures to multiple air pollutants (black carbon (BC), carbon monoxide (CO), ultrafine particle number concentration (PNC), and fine particulate matter (PM2.5)) during commutes between the home and workplace for 45 participants. Participants completed 8 days of commuting by car and bicycle on direct and alternative (reduced traffic) routes. Mean within-person exposures to BC, PM2.5, and PNC were higher when commuting by cycling than when driving, but mean CO exposure was lower when cycling. Exposures to CO and BC were reduced when commuting along alternative routes. When cumulative exposure was considered, the benefits from cycling were attenuated, in the case of CO, or exacerbated, in the case of particulate exposures, owing to the increased duration of the commute. Although choice of route can reduce mean exposure, the effect of route length and duration often offsets these reductions when cumulative exposure is considered. Furthermore, increased ventilation rate when cycling may result in a more harmful dose than inhalation at a lower ventilation rate.

Influence of walking route choice on primary school children's exposure to air pollution--A proof of concept study using simulation.

This study developed a walking network for the Greater Manchester area (UK). The walking network allows routes to be calculated either based on the shortest duration or based on the lowest cumulative nitrogen dioxide (NO2) or particulate matter (PM10) exposure. The aim of this study was to analyse the costs and benefits of faster routes versus lower pollution exposure for walking routes to primary schools. Random samples of primary schools and residential addresses were used to generate 100,000 hypothetical school routes. For 60% (59,992) and 40% (40,460) an alternative low NO2 and PM10 route was found, respectively. The median change in travel time (NO2: 4.5s, PM10: 0.5s) and average route exposure (NO2: -0.40 µg/m(3), PM10: -0.03 µg/m(3)) was small. However, quantile regression analysis indicated that for 50% of routes a 1% increase in travel time was associated with a 1.5% decrease in NO2 and PM10 exposure. The results of this study suggest that the relative decrease in pollution exposure on low pollution routes tends to be greater than the relative increase in route length. This supports the idea that a route planning tool identifying less polluted routes to primary schools could help deliver potential health benefits for children.

Spatial variation of PM elemental composition between and within 20 European study areas--Results of the ESCAPE project.

An increasing number of epidemiological studies suggest that adverse health effects of air pollution may be related to particulate matter (PM) composition, particularly trace metals. However, we lack comprehensive data on the spatial distribution of these elements. We measured PM2.5 and PM10 in twenty study areas across Europe in three seasonal two-week periods over a year using Harvard impactors and standardized protocols. In each area, we selected street (ST), urban (UB) and regional background (RB) sites (totaling 20) to characterize local spatial variability. Elemental composition was determined by energy-dispersive X-ray fluorescence analysis of all PM2.5 and PM10 filters. We selected a priori eight (Cu, Fe, K, Ni, S, Si, V, Zn) well-detected elements of health interest, which also roughly represented different sources including traffic, industry, ports, and wood burning. PM elemental composition varied greatly across Europe, indicating different regional influences. Average street to urban background ratios ranged from 0.90 (V) to 1.60 (Cu) for PM2.5 and from 0.93 (V) to 2.28 (Cu) for PM10. Our selected PM elements were variably correlated with the main pollutants (PM2.5, PM10, PM2.5 absorbance, NO2 and NOx) across Europe: in general, Cu and Fe in all size fractions were highly correlated (Pearson correlations above 0.75); Si and Zn in the coarse fractions were modestly correlated (between 0.5 and 0.75); and the remaining elements in the various size fractions had lower correlations (around 0.5 or below). This variability in correlation demonstrated the distinctly different spatial distributions of most of the elements. Variability of PM10_Cu and Fe was mostly due to within-study area differences (67% and 64% of overall variance, respectively) versus between-study area and exceeded that of most other traffic-related pollutants, including NO2 and soot, signaling the importance of non-tailpipe (e.g., brake wear) emissions in PM.