A random forest approach to estimate daily particulate matter, nitrogen dioxide, and ozone at fine spatial resolution in Sweden

Air pollution is one of the leading causes of mortality worldwide. An accurate assessment of its spatial and temporal distribution is mandatory to conduct epidemiological studies able to estimate long-term (e.g., annual) and short-term (e.g., daily) health effects. While spatiotemporal models for particulate matter (PM) have been developed in several countries, estimates of daily nitrogen dioxide (NO 2 ) and ozone (O 3 ) concentrations at high spatial resolution are lacking, and no such models have been developed in Sweden. We collected data on daily air pollutant concentrations from routine monitoring networks over the period 2005-2016 and matched them with satellite data, dispersion models, meteorological parameters, and land-use variables. We developed a machine-learning approach, the random forest (RF), to estimate daily concentrations of PM 10 (PM<10 microns), PM 2.5 (PM<2.5 microns), PM 2.5-10 (PM between 2.5 and 10 microns), NO 2 , and O 3 for each squared kilometer of Sweden over the period 2005-2016. Our models were able to describe between 64% (PM 10 ) and 78% (O 3 ) of air pollutant variability in held-out observations, and between 37% (NO 2 ) and 61% (O 3 ) in held-out monitors, with no major differences across years and seasons and better performance in larger cities such as Stockholm. These estimates will allow to investigate air pollution effects across the whole of Sweden, including suburban and rural areas, previously neglected by epidemiological investigation

Seasonal patterns of outdoor PM infiltration into indoor environments: review and meta-analysis of available studies from different climatological zones in Europe

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Individual Exposure to NO2 in Relation to Spatial and Temporal Exposure Indices in Stockholm, Sweden: The INDEX Study

Epidemiology studies of health effects from air pollution, as well as impact assessments, typically rely on ambient monitoring data or modelled residential levels. The relationship between these and personal exposure is not clear. To investigate personal exposure to NO2 and its relationship with other exposure metrics and time-activity patterns in a randomly selected sample of healthy working adults (20–59 years) living and working in Stockholm. Personal exposure to NO2 was measured with diffusive samplers in sample of 247 individuals. The 7-day average personal exposure was 14.3 µg/m3 and 12.5 µg/m3 for the study population and the inhabitants of Stockholm County, respectively. The personal exposure was significantly lower than the urban background level (20.3 µg/m3). In the univariate analyses the most influential determinants of individual exposure were long-term high-resolution dispersion-modelled levels of NO2 outdoors at home and work, and concurrent NO2 levels measured at a rural location, difference between those measured at an urban background and rural location and difference between those measured in busy street and at an urban background location, explaining 20, 16, 1, 2 and 4% (R2) of the 7-day personal NO2 variation, respectively. A regression model including these variables explained 38% of the variation in personal NO2 exposure. We found a small improvement by adding time-activity variables to the latter model (R2 = 0.44). The results adds credibility primarily to long-term epidemiology studies that utilise long-term indices of NO2 exposure at home or work, but also indicates that such studies may still suffer from exposure misclassification and dilution of any true effects. In contrast, urban background levels of NO2 are poorly related to individual exposure

Is it all about storytelling? Living and learning hereditary cancer on Twitter

Storytelling has long been used as a theoretical framework for understanding how we share information and learn about health – and illness – online. But is it all about storytelling on social media platforms? To explore how and to what extent personal stories shape health content on these platforms, the article presents an analysis of tweets discussing the BRCA gene mutation – a hereditary cancer condition. Theoretically, the study advances a new conceptual framework to explore social media practices within issue–based and long–lived social media threads. Methodologically, it develops a qualitative, platform–oriented discourse analytic approach. Findings show that non narrative content is actually more common than storytelling in Twitter conversations about BRCA, with a number of patient advocates acting as gatekeepers of scientific information. Most BRCA storytelling is mediated and shared in third person, with those at the heart of these stories becoming exemplars within the BRCA ‘subculture’

Long-term exposure to low ambient air pollution concentrations and mortality among 28 million people: results from seven large European cohorts within the ELAPSE project

Background Long-term exposure to ambient air pollution has been associated with premature mortality, but associations at concentrations lower than current annual limit values are uncertain. We analysed associations between low-level air pollution and mortality within the multicentre study Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE). Methods In this multicentre longitudinal study, we analysed seven population-based cohorts of adults (age ≥30 years) within ELAPSE, from Belgium, Denmark, England, the Netherlands, Norway, Rome (Italy), and Switzerland (enrolled in 2000-11; follow-up until 2011-17). Mortality registries were used to extract the underlying cause of death for deceased individuals. Annual average concentrations of fine particulate matter (PM2·5), nitrogen dioxide (NO2), black carbon, and tropospheric warm-season ozone (O3) from Europe-wide land use regression models at 100 m spatial resolution were assigned to baseline residential addresses. We applied cohort-specific Cox proportional hazard models with adjustment for area-level and individual-level covariates to evaluate associations with non-accidental mortality, as the main outcome, and with cardiovascular, non-malignant respiratory, and lung cancer mortality. Subset analyses of participants living at low pollutant concentrations (as per predefined values) and natural splines were used to investigate the concentration-response function. Cohort-specific effect estimates were pooled in a random-effects meta-analysis. Findings We analysed 28 153 138 participants contributing 257 859 621 person-years of observation, during which 3 593 741 deaths from non-accidental causes occurred. We found significant positive associations between non-accidental mortality and PM2·5, NO2, and black carbon, with a hazard ratio (HR) of 1·053 (95% CI 1·021-1·085) per 5 μg/m3 increment in PM2·5, 1·044 (1·019-1·069) per 10 μg/m3 NO2, and 1·039 (1·018-1·059) per 0·5 × 10−5/m black carbon. Associations with PM2·5, NO2, and black carbon were slightly weaker for cardiovascular mortality, similar for non-malignant respiratory mortality, and stronger for lung cancer mortality. Warm-season O3 was negatively associated with both non-accidental and cause-specific mortality. Associations were stronger at low concentrations: HRs for non-accidental mortality at concentrations lower than the WHO 2005 air quality guideline values for PM2·5 (10 μg/m3) and NO2 (40 μg/m3) were 1·078 (1·046-1·111) per 5 μg/m3 PM2·5 and 1·049 (1·024-1·075) per 10 μg/m3 NO2. Similarly, the association between black carbon and non-accidental mortality was highest at low concentrations, with a HR of 1·061 (1·032-1·092) for exposure lower than 1·5× 10−5/m, and 1·081 (0·966-1·210) for exposure lower than 1·0× 10−5/m. Interpretation Long-term exposure to concentrations of PM2·5 and NO2 lower than current annual limit values was associated with non-accidental, cardiovascular, non-malignant respiratory, and lung cancer mortality in seven large European cohorts. Continuing research on the effects of low concentrations of air pollutants is expected to further inform the process of setting air quality standards in Europe and other global regions

Methodological aspects of a GIS-based environmental health inspection program used in the Athens 2004 Olympic and Para Olympic Games

BACKGROUND: The use of geographical information system (GIS) technologies in public health surveillance is gradually gaining momentum around the world and many applications have already been reported in the literature. In this study, GIS technology was used to help county departments of Public Health to implement environmental health surveillance for the Athens 2004 Olympic and Para Olympic Games. METHODS: In order to assess the workload in each Olympic county, 19 registry forms and 17 standardized inspection forms were developed to register and inspect environmental health items requiring inspection (Hotels, restaurants, swimming pools, water supply system etc), respectively. Furthermore, related databases were created using Epi Info 2002 and a geographical information system (GIS) were used to implement an integrated Environmental Health inspection program. The project was conducted in Athens by the Olympic Planning Unit (OPU) of the National School of Public Health, in close cooperation with the Ministry of Health and Social Solidarity and the corresponding departments of Public Health in all municipalities that were scheduled to host events during the Athens 2004 Olympic and Para Olympic games. RESULTS: A total of 44,741 premises of environmental health interest were geocoded into GIS databases and several electronic maps were developed. Using such maps in association with specific criteria, we first identified the maximum workload required to execute environmental health inspections in all premises within the eleven Olympic County Departments of Public Health. Six different scenarios were created for each county, based on devised algorithms in order to design the most effective and realistic inspection program using the available inspectors from each municipality. Furthermore, GIS applications were used to organize the daily inspection program for the Olympic games, provide coloured displays of the inspection results and link those results with the public health surveillance of specific cases or outbreak investigation. CONCLUSION: Our computerised program exhibited significant efficiency in facilitating the prudent use of public health resources in implementing environmental health inspections in densely populated urban areas as well as in rural counties. Furthermore, the application of simple algorithms in integrating human and other resources provided tailored and cost-effective applications to different public health agencies

Perceived annoyance and asthmatic symptoms in relation to vehicle exhaust levels outside home: a cross-sectional study

which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background: Exhaust emissions from vehicles is a well known problem with both epidemiological and experimental studies showing increasing adverse health effects with elevating levels. Many of the studies concerning vehicle exhausts and health are focused on health outcomes where the proportion attributed to exhaust is low, while there is less information on early and more frequent subjective indicators of adverse effects. Methods: The primary aim of this study was to study perceived annoyance in relation to vehicle exhaust concentrations using modelled levels of nitrogen dioxide outside the home as an indicator with high spatial resolution. Almost 2800 persons in a random sample from three Swedish cities (Umea, Uppsala and Gothenburg) responded to our questionnaire. Questions were asked to determine the degree of annoyance related to vehicle exhausts and also the prevalence of irritating and asthmatic symptoms. Exposure was described for each participants home address by meteorological dispersion models with a 50 meter resolution. Results: We found a significant increase of peoples &apos; self-assessed annoyance with rising levels of NO2. The odds of being very annoyed by vehicle exhausts increased by 14 % per 1 µg/m3 increas

Prediction and analysis of near-road concentrations using a reduced-form emission/dispersion model

<p>Abstract</p> <p>Background</p> <p>Near-road exposures of traffic-related air pollutants have been receiving increased attention due to evidence linking emissions from high-traffic roadways to adverse health outcomes. To date, most epidemiological and risk analyses have utilized simple but crude exposure indicators, most typically proximity measures, such as the distance between freeways and residences, to represent air quality impacts from traffic. This paper derives and analyzes a simplified microscale simulation model designed to predict short- (hourly) to long-term (annual average) pollutant concentrations near roads. Sensitivity analyses and case studies are used to highlight issues in predicting near-road exposures.</p> <p>Methods</p> <p>Process-based simulation models using a computationally efficient reduced-form response surface structure and a minimum number of inputs integrate the major determinants of air pollution exposures: traffic volume and vehicle emissions, meteorology, and receptor location. We identify the most influential variables and then derive a set of multiplicative submodels that match predictions from "parent" models MOBILE6.2 and CALINE4. The assembled model is applied to two case studies in the Detroit, Michigan area. The first predicts carbon monoxide (CO) concentrations at a monitoring site near a freeway. The second predicts CO and PM_2.5concentrations in a dense receptor grid over a 1 km²area around the intersection of two major roads. We analyze the spatial and temporal patterns of pollutant concentration predictions.</p> <p>Results</p> <p>Predicted CO concentrations showed reasonable agreement with annual average and 24-hour measurements, e.g., 59% of the 24-hr predictions were within a factor of two of observations in the warmer months when CO emissions are more consistent. The highest concentrations of both CO and PM_2.5were predicted to occur near intersections and downwind of major roads during periods of unfavorable meteorology (e.g., low wind speeds) and high emissions (e.g., weekday rush hour). The spatial and temporal variation among predicted concentrations was significant, and resulted in unusual distributional and correlation characteristics, including strong negative correlation for receptors on opposite sides of a road and the highest short-term concentrations on the "upwind" side of the road.</p> <p>Conclusions</p> <p>The case study findings can likely be generalized to many other locations, and they have important implications for epidemiological and other studies. The reduced-form model is intended for exposure assessment, risk assessment, epidemiological, geographical information systems, and other applications.</p

Analyses of cerebral microdialysis in patients with traumatic brain injury: relations to intracranial pressure, cerebral perfusion pressure and catheter placement

<p>Abstract</p> <p>Background</p> <p>Cerebral microdialysis (MD) is used to monitor local brain chemistry of patients with traumatic brain injury (TBI). Despite an extensive literature on cerebral MD in the clinical setting, it remains unclear how individual levels of real-time MD data are to be interpreted. Intracranial pressure (ICP) and cerebral perfusion pressure (CPP) are important continuous brain monitors in neurointensive care. They are used as surrogate monitors of cerebral blood flow and have an established relation to outcome. The purpose of this study was to investigate the relations between MD parameters and ICP and/or CPP in patients with TBI.</p> <p>Methods</p> <p>Cerebral MD, ICP and CPP were monitored in 90 patients with TBI. Data were extensively analyzed, using over 7,350 samples of complete (hourly) MD data sets (glucose, lactate, pyruvate and glycerol) to seek representations of ICP, CPP and MD that were best correlated. MD catheter positions were located on computed tomography scans as pericontusional or nonpericontusional. MD markers were analyzed for correlations to ICP and CPP using time series regression analysis, mixed effects models and nonlinear (artificial neural networks) computer-based pattern recognition methods.</p> <p>Results</p> <p>Despite much data indicating highly perturbed metabolism, MD shows weak correlations to ICP and CPP. In contrast, the autocorrelation of MD is high for all markers, even at up to 30 future hours. Consequently, subject identity alone explains 52% to 75% of MD marker variance. This indicates that the dominant metabolic processes monitored with MD are long-term, spanning days or longer. In comparison, short-term (differenced or Δ) changes of MD vs. CPP are significantly correlated in pericontusional locations, but with less than 1% explained variance. Moreover, CPP and ICP were significantly related to outcome based on Glasgow Outcome Scale scores, while no significant relations were found between outcome and MD.</p> <p>Conclusions</p> <p>The multitude of highly perturbed local chemistry seen with MD in patients with TBI predominately represents long-term metabolic patterns and is weakly correlated to ICP and CPP. This suggests that disturbances other than pressure and/or flow have a dominant influence on MD levels in patients with TBI.</p