Respiratory hospital admission risk near large composting facilities

AbstractBackgroundLarge-scale composting can release bioaerosols in elevated quantities, but there are few studies of health effects on nearby communities.MethodsA cross-sectional ecological small area design was used to examine risk of respiratory hospital admissions within 2500m of all 148 English large-scale composting facilities in 2008–10. Statistical analyses used a random intercept Poisson regression model at Census Output Area (COA) level (mean population 310). Models were adjusted for age, sex, deprivation and tobacco sales.ResultsAnalysing 34,963 respiratory hospital admissions in 4656 COAs within 250–2500m of a site, there were no significant trends using pre-defined distance bands of >250–750m, >750–1500m and >1500–2500m. Using a continuous measure of distance, there was a small non-statistically significant (p=0.054) association with total respiratory admissions corresponding to a 1.5% (95% CI: 0.0–2.9%) decrease in risk if moving from 251m to 501m. There were no significant associations for subgroups of respiratory infections, asthma or chronic obstructive pulmonary disease.ConclusionThis national study does not provide evidence for increased risks of respiratory hospital admissions in those living beyond 250m of an outdoor composting area perimeter. Further work using better measures of exposure and exploring associations with symptoms and disease prevalence, especially in vulnerable groups, is recommended to support regulatory approaches

Spatial and temporal variations in PM10 concentrations between 2010-2017 in South Africa

Particulate matter less than or equal to 10 mum in aerodynamic diameter (PM10 microg/m(3)) is a priority air pollutant and one of the most widely monitored ambient air pollutants in South Africa. This study analyzed PM10 from monitoring 44 sites across four provinces of South Africa (Gauteng, Mpumalanga, Western Cape and KwaZulu-Natal) and aimed to present spatial and temporal variation in the PM10 concentration across the provinces. In addition, potential influencing factors of PM10 variations around the three site categories (Residential, Industrial and Traffic) were explored. The spatial trend in daily PM10 concentration variation shows PM10 concentration can be 5.7 times higher than the revised 2021 World Health Organization annual PM10 air quality guideline of 15 microg/m(3) in Gauteng province during the winter season. Temporally, the highest weekly PM10 concentrations of 51.4 microg/m(3), 46.8 microg/m(3), 29.1 microg/m(3) and 25.1 microg/m(3) at Gauteng, Mpumalanga, KwaZulu-Natal and Western Cape Province were recorded during the weekdays. The study results suggest a decrease in the change of annual PM10 levels at sites in Gauteng and Mpumalanga Provinces. An increased change in annual PM10 levels was reported at most sites in Western Cape and KwaZulu-Natal

Spatial variability of nitrogen dioxide and formaldehydeand residential exposure of children in the industrial area of Viadana, Northern Italy

Chipboard production is a source of ambient air pollution. We assessed the spatial variability of outdoor pollutants and residentialexposure of children living in proximity to the largest chipboard industry in Italy and evaluated the reliability of exposureestimates obtained from a number of available models. We obtained passive sampling data on NO2and formaldehyde collectedby the Environmental Protection Agency of Lombardy region at 25 sites in the municipality of Viadana during 10 weeks (2017-2018) and compared NO2measurements with average weekly concentrations from continuous monitors. We compared interpo-lated NO2and formaldehyde surfaces with previous maps for 2010. We assessed the relationship between residential proximity tothe industry and pollutant exposures assigned using these maps, as well as other available countrywide/continental models basedon routine data on NO2, PM10, andPM2.5. The correlation between NO2concentrations from continuous and passive samplingwas high (Pearson'sr= 0.89), although passive sampling underestimated NO2especially during winter. For both 2010 and 2017-2018, we observed higher NO2and formaldehyde concentrations in the south of Viadana, with hot-spots in proximity to theindustry. PM10and PM2.5exposures were higher for children at 3.5 km to theindustry, whereas NO2exposure was higher at 1-1.7 km to the industry. Road and population densities were also higher close tothe industry. Findings from a variety of exposure models suggest that children living in proximity to the chipboard industry inViadana are more exposed to air pollution and that exposure gradients are relatively stable over time

Explorative assessment of the temperature-mortality association to support health-based heat-warning thresholds: a national case-crossover study in Switzerland

Defining health-based thresholds for effective heat warnings is crucial for climate change adaptation strategies. Translating the non-linear function between heat and health effects into an effective threshold for heat warnings to protect the population is a challenge. We present a systematic analysis of heat indicators in relation to mortality. We applied distributed lag non-linear models in an individual-level case-crossover design to assess the effects of heat on mortality in Switzerland during the warm season from 2003 to 2016 for three temperature metrics (daily mean, maximum, and minimum temperature), and various threshold temperatures and heatwave definitions. Individual death records with information on residential address from the Swiss National Cohort were linked to high-resolution temperature estimates from 100 m resolution maps. Moderate (90th percentile) to extreme thresholds (99.5th percentile) of the three temperature metrics implied a significant increase in mortality (5 to 38%) in respect of the median warm-season temperature. Effects of the threshold temperatures on mortality were similar across the seven major regions in Switzerland. Heatwave duration did not modify the effect when considering delayed effects up to 7 days. This nationally representative study, accounting for small-scale exposure variability, suggests that the national heat-warning system should focus on heatwave intensity rather than duration. While a different heat-warning indicator may be appropriate in other countries, our evaluation framework is transferable to any country

Nutrition interventions for healthy ageing across the lifespan:a conference report

Thanks to advances in modern medicine over the past century, the world's population has experienced a marked increase in longevity. However, disparities exist that lead to groups with both shorter lifespan and significantly diminished health, especially in the aged. Unequal access to proper nutrition, healthcare services, and information to make informed health and nutrition decisions all contribute to these concerns. This in turn has hastened the ageing process in some and adversely affected others' ability to age healthfully. Many in developing as well as developed societies are plagued with the dichotomy of simultaneous calorie excess and nutrient inadequacy. This has resulted in mental and physical deterioration, increased non-communicable disease rates, lost productivity and quality of life, and increased medical costs. While adequate nutrition is fundamental to good health, it remains unclear what impact various dietary interventions may have on improving healthspan and quality of life with age. With a rapidly ageing global population, there is an urgent need for innovative approaches to health promotion as individual's age. Successful research, education, and interventions should include the development of both qualitative and quantitative biomarkers and other tools which can measure improvements in physiological integrity throughout life. Data-driven health policy shifts should be aimed at reducing the socio-economic inequalities that lead to premature ageing. A framework for progress has been proposed and published by the World Health Organization in its Global Strategy and Action Plan on Ageing and Health. This symposium focused on the impact of nutrition on this framework, stressing the need to better understand an individual's balance of intrinsic capacity and functional abilities at various life stages, and the impact this balance has on their mental and physical health in the environments they inhabit

Residential radon - Comparative analysis of exposure models in Switzerland.

Residential radon exposure is a major public health issue in Switzerland due to the known association between inhaled radon progeny and lung cancer. To confirm recent findings of an association with skin cancer mortality, an updated national radon model is needed. The aim of this study was to derive the best possible residential radon prediction model for subsequent epidemiological analyses. Two different radon prediction models were developed (linear regression model vs. random forest) using ca. 80,000 measurements in the Swiss Radon Database (1994-2017). A range of geographic predictors and building specific predictors were considered in the 3-D models (x,y, floor of dwelling). A five-fold modelling strategy was used to evaluate the robustness of each approach, with models developed (80% measurement locations) and validated (20%) using standard diagnostics. Random forest consistently outperformed the linear regression model, with higher Spearman's rank correlation (51% vs. 36%), validation coefficient of determination (R <sup>2</sup> 31% vs. 15%), lower root mean square error (RMSE) and lower fractional bias. Applied to the population of 5.4 million adults in 2000, the random forest resulted in an arithmetic mean (standard deviation) of 75.5 (31.7) Bq/m <sup>3</sup> , and indicated a respective 16.1% and 0.1% adults with predicted radon concentrations exceeding the World Health Organization (100 Bq/m <sup>3</sup> ) and Swiss (300 Bq/m <sup>3</sup> ) reference values

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

Comparing methods to impute missing daily ground-level PM10 concentrations between 2010-2017 in South Africa

Good quality and completeness of ambient air quality monitoring data is central in supporting actions towards mitigating the impact of ambient air pollution. In South Africa, however, availability of continuous ground-level air pollution monitoring data is scarce and incomplete. To address this issue, we developed and compared different modeling approaches to impute missing daily average particulate matter (PM10) data between 2010 and 2017 using spatiotemporal predictor variables. The random forest (RF) machine learning method was used to explore the relationship between average daily PM10 concentrations and spatiotemporal predictors like meteorological, land use and source-related variables. National (8 models), provincial (32) and site-specific (44) RF models were developed to impute missing daily PM10 data. The annual national, provincial and site-specific RF cross-validation (CV) models explained on average 78%, 70% and 55% of ground-level PM10 concentrations, respectively. The spatial components of the national and provincial CV RF models explained on average 22% and 48%, while the temporal components of the national, provincial and site-specific CV RF models explained on average 78%, 68% and 57% of ground-level PM10 concentrations, respectively. This study demonstrates a feasible approach based on RF to impute missing measurement data in areas where data collection is sparse and incomplete

Predicting fine-scale daily NO2 over Mexico city using an ensemble modeling approach

In recent years, there has been growing interest in developing air pollution prediction models to reduce exposure measurement error in epidemiologic studies. However, efforts for localized, fine-scale prediction models have been predominantly focused in the United States and Europe. Furthermore, the availability of new satellite instruments such as the TROPOsopheric Monitoring Instrument (TROPOMI) provides novel opportunities for modeling efforts. We estimated daily ground-level nitrogen dioxide (NO2) concentrations in the Mexico City Metropolitan Area at 1-km2 grids from 2005 to 2019 using a four-stage approach. In stage 1 (imputation stage), we imputed missing satellite NO2 column measurements from the Ozone Monitoring Instrument (OMI) and TROPOMI using the random forest (RF) approach. In stage 2 (calibration stage), we calibrated the association of column NO2 to ground-level NO2 using ground monitors and meteorological features using RF and extreme gradient boosting (XGBoost) models. In stage 3 (prediction stage), we predicted the stage 2 model over each 1-km2 grid in our study area, then ensembled the results using a generalized additive model (GAM). In stage 4 (residual stage), we used XGBoost to model the local component at the 200-m2 scale. The cross-validated R2 of the RF and XGBoost models in stage 2 were 0.75 and 0.86 respectively, and 0.87 for the ensembled GAM. Cross-validated root-mean-squared error (RMSE) of the GAM was 3.95 μg/m3. Using novel approaches and newly available remote sensing data, our multi-stage model presented high cross-validated fits and reconstructs fine-scale NO2 estimates for further epidemiologic studies in Mexico City